Application of a Scheme for Validating Clouds in an Operational ...

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Application of a Scheme for Validating Clouds in an Operational Global NWP Model L. RIKUS Australian Bureau of Meteorology Research Centre, Melbourne, Victoria, Australia (Manuscript received 6 April 1996, in final form 28 October 1996) ABSTRACT The cloud fields in numerical weather prediction (NWP) models are often validated by comparing them with climatological datasets. The aim of an operational NWP model is to produce instantaneous representations of the relevant meteorological fields at the time corresponding to the analysis or forecast. Thus model cloud fields should ideally be validated against the instantaneous real cloud fields. At the Bureau of Meteorology a realtime cloud validation scheme has been developed and has been in operation since late 1991. It is based on a comparison of infrared brightness temperature from geostationary satellites and the corresponding brightness temperature calculated from the model’s thermodynamic fields. Although the model assumes black cirrus, a comparison using temperature-dependent cirrus emissivity was also implemented to provide more realistic values of brightness temperature. To aid in the interpretation of the comparison, additional cloud fields are generated from the satellite data using a simple cloud clearing algorithm and also by defining pseudocloud height classes based on the temperature structure of the model. An overview of the results of the validation for the analyses from the operational medium-range forecast model over the period from May 1994 to April 1995 are described. Overall the model analysis fields lack the large-scale continuity of the satellite data. Areas of large-scale convection over South America, Africa, and the central Pacific Ocean warm pool are not simulated well. The model’s ITCZ is too disorganized, lacks strength, and is often misplaced.

1. Introduction Sensitivity to a model’s cloud–radiation parameterization manifests itself in model forecasts on timescales of about 5 days and hence assumes some importance in medium- to long-range forecasts. As a consequence, cloud–radiation processes and their associated feedbacks play an integral role in the assimilation cycle in operational numerical weather prediction (NWP) models, particularly in data-sparse regions where the model’s climatology most strongly influences the first-guess field of the analysis cycle. The parameterization of the effect of clouds on these processes consists of two components: the specification of the three-dimensional distribution of its cloud field and the specification of cloud optical properties. Unfortunately both components involve aspects that are currently not well understood (Cess et al. 1990). Parameterization schemes for cloud amount have evolved from simple prescribed zonal average cloud (Manabe and Holloway 1971) to diagnostic schemes based on relative humidity and static stability (Slingo 1987). These have generally been coupled to prescribed cloud optical properties or simple schemes based on liquid water amounts derived as a fixed fraction

Corresponding author address: Dr. L. Rikus, Bureau of Meteorology Research Centre, G.P.O. Box 1289K, Melbourne, Vic 3001, Australia. E-mail: [email protected]

q 1997 American Meteorological Society

of saturation mixing ratio (Geleyn 1981). Recently schemes for prognostic water (Smith 1990; Tiedke 1993) have been developed. These schemes seek to unify all aspects of a model’s hydrology by the specification of a cloud liquid water/ice field that can then be used to define (in conjunction with droplet size information) cloud albedo, absorptance, and emissivity (Stephens 1978; Slingo 1989). The cloud parameterization problem in an NWP model differs from that for a climate model. For the latter it is sufficient to correctly simulate the time average of the effects of cloud and related feedbacks over periods of months to years. In a climate model errors in the cloud climatology can be attributed to problems in the model’s parameterization of hydrological processes. In an NWP model the state of the model atmosphere at the analysis time and subsequently throughout the forecasts should represent the state of the real atmosphere; accordingly, the cloud scheme is required to accurately simulate real, large-scale cloud processes and model the associated feedbacks. The ultimate aim is for the model and its assimilation system to accurately represent the instantaneous cloud fields at the time of the analysis or forecast. This is difficult for a number of reasons. First, a cloud scheme must depend on the model’s description of the hydrological cycle that is arguably one of the weakest components of modern NWP models due to the quality of moisture analyses and limitations in the parameterization of physical processes such as convection.

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Second, it will depend on the amount and quality of the data available to the analysis system and also details of the analyzed fields. Unfortunately, water vapor is probably the meteorological field most difficult to analyze, particularly in the upper troposphere. This is due partly to its discrete nature, which is not conducive to current analysis methods that assume continuity in most fields, and partly to a lack of sufficient, reliable data to describe it adequately. This latter concern is complicated by the fact that the current operational satellite moisture retrievals that provide most of the global mid- to uppertroposphere moisture data for the assimilation cycle are restricted to clear-sky regions. In addition, an operational NWP model must make a trade-off between the complexity it can incorporate in a parameterization scheme and the amount of detail required to realistically characterize the physical processes involved. This tradeoff must be tempered by the lack of knowledge of how cloud really behaves. These considerations raise the question of validation; how is the impact of a cloud parameterization scheme on forecast skill to be measured? Such validation is necessary to judge the efficacy of modifications and to judge the realism of modeled cloud behavior. This is especially true in the context of a weather model where complex feedback processes make it difficult to judge the behavior of any single component of the forecast system using the standard forecast verification method of comparing rms errors, anomaly correlation, etc. A common method of validating model cloud is to compare time-averaged results with a cloud climatology. A number of monthly and longer-term climatologies exist (e.g., Warren et al. 1986, 1988; Rossow and Schiffer 1991) but they all differ, partly due to the disparate forms of observational data used and partly because interannual variability seems to be very large. It is possible to choose special periods for which a comprehensive satellite dataset is available and such experiments are useful, particularly for providing insight into the effects of model changes on forecast performance. This approach is not optimal for an operational NWP model in which the meteorological products need to be validated continuously, preferably as close to their valid time as possible. If the validation scheme is run as part of the operational suite, it tests the model over the full range of synoptic conditions. In addition, in an environment of progressive model upgrades a real-time scheme keeps pace with the current operational model version and provides immediate feedback on its performance. One consequence of this is that the cloud parameterization can be modified in a timely fashion to correct errors as they occur. In fact, the parameterization could even be based on such validation, for example, objectively defining the functional relationship between relative humidity and cloud amount on the basis of statistical results (Rikus and Hart 1989; Mitchell and Hahn 1990). For these reasons, the best choice for an NWP model would be to validate against real-time cloud data,

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with particular emphasis on satellite data. These are readily available, essentially global in extent, and can be processed to produce fields at spatial scales well suited to large-scale models. While real-time satellite data has some limitations in comparison with processed datasets such as ISCCP (International Satellite Cloud Climatology Project), these are offset by its timeliness. The next section of this paper briefly reviews the BMRC operational global NWP model, with particular emphasis on the model’s diagnostic cloud parameterization. The following section discusses the implementation of the real-time validation scheme. This is followed by descriptions of the two facets of the scheme, discussion of the results, and the implications for the model’s simulation of cloud. The paper concludes with a summary of the results. 2. The BMRC global spectral model a. Model definition The operational BMRC Global Assimilation and Prediction System (GASP) (Seaman et al. 1995; Bourke et al. 1995) is currently used for 6-h assimilation and 7-day predictions and to supply boundary conditions for shortrange regional forecast models. The operational assimilation cycle consists of intermittent (6 h) multivariate statistical interpolation (MVSI), with analyses that are archived every six model hours. When the cloud validation scheme was first implemented in November 1991, the model used 9 sigma levels in the vertical but was upgraded to 19 levels in December 1992, in conjunction with modifications to some of the physical parameterization schemes and the implementation of the MVSI analysis scheme. The horizontal spectral resolution was initially rhomboidal wavenumber 31 but changed to rhomboidal 53 in March 1994 and to T79 in late 1995. The basic physical parameterizations for the model have been described in detail elsewhere (Hart et al. 1990). The modeling of radiative transfer is based on the Fels and Schwarzkopf (1975) parameterization scheme in the longwave and Lacis and Hansen (1974) in the shortwave. In implementing the increased vertical resolution of 19 levels, the main change affecting the model’s hydrology was to the specification of the moistening parameter (usually referred to as the b parameter) in the Kuo scheme (Kuo 1974; Anthes 1977), resulting in a moister troposphere and less convective heating (Tada et al. 1989). Another relevant change was in the assimilation of water vapor. Moisture was assimilated up to the 0.336 sigma level in the 9-level model, but only up to the 0.400 sigma level in the 19-level model, although the lack of reliable mid- to upper-tropospheric moisture data probably minimizes the impact of this change. During the period of operation of the nine-level model the sea surface temperatures (SSTs) were input from a climatological dataset (Alexander and Mobley

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FIG. 2. Time series for the total cloud amount averaged over the global model grid for each month. The grid average used equal weighting for each point. The thick line is a repeated annual composite of the ISCCP total cloud for the period of July 1983 to September 1988. The other curves correspond to the model diagnosed cloud at the analysis time (Anal) and for the 24-h forecast (24 Forc).

FIG. 1. The sigma-level structure (sigma levels multiplied by 1000) of the BMRC global model in its 9-level (left) and its 19-level (right) forms. The shading indicates the levels at which relative humidity– dependent clouds were allowed to form in the three layers. The original 19-level cloud model (far right) was used in January and February 1992. The thin cloud model was used thereafter.

scheme detrains moisture at the appropriate levels and increases the relative humidity sufficiently to allow the radiatively important anvil clouds to be diagnosed by the scheme. An additional convective cloud parameterization (e.g., Slingo 1987) would introduce more degrees of freedom with the accompanying increase in tunable parameters but would also tend to mask systematic errors in the convection scheme. Additional low cloud, corresponding to stratocumulus that is subgrid scale in the vertical (Slingo 1987), is diagnosed at the lowest sigma level of the model in the low cloud layer according to CST 5 min[233.33(G 2 0.03), 1.0],

1976) that was kept fixed for each month. The 19-level model uses SSTs updated weekly from a real-time dataset (Smith 1995). b. The model’s cloud scheme In the diagnostic cloud scheme, clouds are partitioned into three height classes with the layer limits and the relevant sigma-level relationships shown in Fig. 1. The maximum relative humidity RHmax(L) in each layer L is used to calculate a cloud amount C(L) for that layer by

5

6

2

[RHmax (L) 2 RHcrit (L)] C(L) 5 max ,0 . [100 2 RHcrit (L)]

(1)

The critical relative humidities (RHcrit) for low and middle clouds are specified as the function of sigma level suggested by Geleyn (1981), namely, RHcrit 5 100[1 2 2(s 2 s2) 1 Ï3s(1 2 3 s 1 2 s2)].

(2)

To ensure adequate cloud amounts in the Tropics, the value of RHcrit for high clouds is set to 40%. Convective clouds are diagnosed only implicitly; the convection

(3)

where G (K hPa ) is the vertical temperature gradient across the pressure layer above the clouds. Here CST is set to zero unless the relative humidity of the bottom level is greater than 60%. The values of the parameters in (3) were chosen to give the best representation of oceanic stratus in the regions southwest of Australia for the nine-level model. The clouds in each layer are restricted to at most one level, except for low clouds, which can span two contiguous levels. These restrictions are a consequence of the small number of levels and the requirements of the radiation scheme in the model. The clouds are assumed to behave like blackbodies in the longwave and have fixed albedos and absorptivities in the shortwave. 21

c. Global cloud fields The time series of monthly averaged global cloud amounts for 0000 UTC from the operational model are shown in Fig. 2 for the period from November 1991 to February 1994. (Note that these are grid averages where all points on the model grid have equal weight, regardless of the actual area they represent; this places more emphasis on the polar regions.) The change in vertical

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resolution (with the attendant model physics changes, etc.) in December 1992 is very evident. Initially the critical relative humidities in the diagnostic cloud scheme were not modified in the change from 9 to 19 levels, resulting in excessive cloud amounts at all levels. This was only partly due to the moister atmosphere resulting from the change in vertical resolution and the changes to the convection scheme. Most of the increase in cloud was due to the fact that a greater number of model levels in each cloud layer means that some levels will be closer to the layer boundaries than is the case for nine levels (as seen in Fig. 1 for the s 5 0.7 and 0.75 levels, for example). Given that relative humidity generally decreases with altitude (with the important exception of the upper tropical troposphere), the introduction of new levels lower down in a cloud layer allows the diagnostic cloud scheme to sample larger average relative humidities and hence diagnose more clouds. An additional consequence is that cloud is preferentially diagnosed at the bottom level in each layer. This occurred almost everywhere for middle clouds and for high clouds poleward of 308 latitude in the 19-level model. Low cloud tended to collect at the top of its layer. The excessive clouds produced by the scheme resulted in an overall cold bias that adversely affected the model. Also, the use of blackbody high cloud above the s 5 0.187 level resulted in excessive radiative heating in the upper-level Tropics. These problems forced the introduction of a modified scheme (thin cloud) in mid-January 1993. In this scheme the clouds were restricted to occur only at the levels closest to the original nine levels (see Fig. 1), resulting in layers in the model in which no clouds were allowed. This change corresponds to the reduction in total cloud amounts in early 1993 shown in Fig. 2. After this change the high cloud amounts adjusted to values close to those in the original model, while the middle cloud was somewhat lower, probably due to the fact that the bottom level in the 19-level thin-cloud scheme (s 5 0.600) was substantially higher than that in the 9-level model (0.664) and due to the strong vertical gradient in this layer the diagnostic cloud scheme sampled lower relative humidities. The change to the ‘‘thin cloud’’ scheme did not reduce the low cloud that showed a substantial increase in the first half of 1993. This was predominately due to the diagnosis of more arctic stratus and appears to be a seasonal effect that was further enhanced by the presence of additional levels near the surface. Apart from reducing the cold bias in the model, the thin-cloud scheme appeared to have no material effect on the standard indicators of model performance (at least in the regions of interest to operational weather forecasting for 5-day forecasts). The effect on longer-range forecasts and the data-sparse regions in the evolving assimilation cycle was not considered and this aspect still requires investigation. The global average diagnosed cloud amount from December 1991 to May 1995 are compared with an annual

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cloud cycle generated by time-averaging the monthly data from ISCCP over the period from July 1983 to August 1988 (Rossow and Schiffer 1991) in Fig. 2. The total cloud amount for the R31/L9 model was about 60%, in reasonable agreement with ISCCP. With the change to 19 levels (after the substantial initial increase that was moderated by the change to thin cloud), the diagnosed total cloud increased to about 65% and this did not change significantly with the subsequent change in horizontal resolution to R53L19. The annual cycle from the NWP system shows maxima around June–July and minima around January (except at the change in vertical resolution in January 1993) and is clearly out of phase with the ISCCP climatology that has peaks in March and August. This disagreement in seasonal cycle is similar to that found in a study of total cloudiness in climate models (Weare et al. 1995). Model global high clouds were initially about 25% for the R31L9 model, compared with the ISCCP high cloud amounts that range from 15% in July 1983 to about 20% in 1988, suggesting that the model overestimates high clouds by up to 5%. The high cloud amount was relatively insensitive to the change in horizontal resolution. The middle cloud fraction underwent a slight increase from about 20% to 23% with the change to R53. This is comparable with the ISCCP amounts suggesting that the model probably underestimates the true middle cloud amount after accounting for the overlap of high cloud in the satellite data. Model low cloud amounts of around 35%–40% for the R31L9 model increased up to around 50% for the 19-level models; the change to ‘‘thin cloud’’ in January–February 1993 provided only a slight interruption to the rising trend. Although cloud overlap prevents the ISCCP low cloud from providing a good low cloud climatology, comparison with other standard cloud climatologies (e.g., Warren et al. 1986,1988) suggests that the model low cloud amount seems quite reasonable in the early part of the record (R31L9), but thereafter the global amount is almost certainly overestimated by between 10% and 20%. This paper concentrates on the period May 1994– April 1995, chosen because it represents a period of stable assimilation and prediction model configuration at the highest resolution available (R53L19) and is therefore free of artifacts due to model changes thereby allowing a clearer picture of the model’s simulation of cloud. The global averages of the model cloud amounts for this period are compared with the corresponding values from the ISCCP data averaged over the period from July 1983 to September 1988 in Table 1. Due to the mismatch both in the actual dates and the length of averaging period between the ISCCP data and the model analyses and allowing for cloud overlap, only the rough assessment that the high clouds are probably overestimated, middle clouds are probably about right, and low clouds possibly overestimated can be made. The geographical distributions of total clouds for July 1994 for the model analyses and the ISCCP July average for 1983

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TABLE 1. Global average of the model cloud fields for analysis times (Anal) and from the 24-h forecast (24 Forc) averaged over May 1994–April 1995 compared with ISCCP values for the July 1983– September 1988 period. All averages are for a linear weight grid.

High clouds Middle clouds Low clouds Total clouds

Anal

24 Forc

ISCCP

25 23 48 63

25 24 50 64

17 19 25 59

to 1987 are shown in Fig. 3. The analysis cloud does show the expected large-scale features such as the ITCZ (intertropical convergence zone), the Southern Hemisphere storm track cloud, and the clear-sky areas over southern and northern Africa and Australia. However, the comparison highlights some deficiencies in the cloud parameterization; in particular, the model appears to severely underestimate the cloud over Asia and over the southern equatorial Pacific and the equatorial Atlantic Ocean. Overall, there appears to be a tendency for the model’s analyses to simulate larger areas of the extremes of totally clear or totally overcast by comparison with individual monthly averages from ISCCP. Again, only a qualitative comparison is possible because of the differences between different cloud datasets and the uncertainties due to interannual variability. To obtain a more quantitative assessment of the model’s cloud scheme, particularly over short time periods, a real-time cloud validation scheme has been developed in BMRC and the next section presents an overview of the scheme. 3. The validation scheme a. Introduction The choice for the satellite dataset to use for validation is dictated by a number of factors. Although satellite-based datasets are available (Rossow and Schiffer 1991) and are excellent for climate modeling purposes, they are not generally available in real time. For the real-time validation scheme described herein, it was decided to implement a simple, local satellite data collection–analysis–assimilation scheme, initially based on GMS-4 data but subsequently extended to include Meteosat and GOES-7 data. The main reason for choosing geostationary satellites is the scope of the data. It is available for a large fraction of the globe at synoptic times corresponding to operational archive times; using geostationary satellites it is almost possible to obtain a synchronous global ‘‘image’’ (Rossow and Schiffer 1991). This is clearly desirable for the validation of a global model. Also matching the model and the navigation and chronology of the image tends to be simpler than is the case with orbiting satellites. The disadvantage of geostationary satellites is that they have only a limited number of spectral bands. For example, the GMS data is limited to the 11-mm IR band if the full diurnal cycle is to be considered. This

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restricts the choice of model variables to be validated. Ideally the choice would be for direct validation of cloud, but the limited spectral information available from the satellite precludes the use of accurate cloud identification schemes. Clearly it is better to use the model fields to generate the radiance (characterized by effective brightness temperature) as seen by the satellite. In normal operation the model does not need to produce the narrowband radiances used by satellite sensors (particularly with the sensor response function folded in), and so for efficiency and logistical reasons it has been necessary to derive such radiances off-line using all the appropriate fields from a standard archive of the model’s fields. For consistency, the off-line code should involve the same spectral information and approximations in radiative transport as that in the model itself. The Fels–Schwarzkopf infrared broadband radiation code has been shown to be accurate in intercomparisons with other radiation codes as well as line-by-line results (Ellingson et al. 1991). Provided the off-line code for the satellite infrared band and the model radiation code have comparable accuracy, disagreement between satellite and model-derived brightness temperature can realistically be attributed to errors in the model’s thermodynamic and cloud fields. Over the four years of operation of the cloud validation scheme, a substantial amount of raw data has been produced. For the purposes of this study only a subset can be considered. Although the model does exhibit some spinup in total diagnosed cloud (as seen in Fig. 2 for the 24-h forecast), the large-scale characteristics of the model cloud fields for forecasts do not differ greatly in character from those present in the analysis. In particular, the spinup for the first 24 h has little effect on the shortcomings apparent in the analysis. By concentrating on the results derived from the analysis fields, the effects of gross forecast errors in the model’s simulation of the hydrological cycle should be minimized, while the impact of the data assimilated during the analysis cycle should be maximized. The 0000 and 1200 UTC results are very similar, and for this reason only the former will be presented in the following discussions. b. Validating brightness temperature It is generally accepted that it is better to validate the cloud–radiation parameterization by deriving variables from the model that can be directly compared with only minimally processed satellite data (Morcrette 1991); such techniques have been used to validate prognostic water schemes (Li and LeTreut 1989). The basic BMRC cloud validation method follows this approach and uses the archived model thermodynamic and diagnosed cloud fields as input for a special infrared (IR) window version of the Fels–Schwarzkopf longwave radiation code to produce brightness temperature fields matching the spectral response of those from the geostationary sat-

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ellites. Apart from navigation, the only processing required for the satellite data itself is to average it onto the model’s physics grid. The R53 grid consists of 162 equally spaced longitudes and 134 latitudes, with a spacing corresponding to a Gaussian integration grid.1 By averaging the satellite data to the model resolution, interpolation errors and problems due to mismatching resolutions are minimized. The validation regions for each satellite were chosen to avoid large viewing angles and the consequent problems with quantitative interpretation of IR satellite data near the edges of the image due to limb effects. The GMS-4 satellite imagery is received in real time by the Bureau of Meteorology, accurately navigated, and then stored in the McIDAS ‘‘area’’ format (Suomi et al. 1983). The IR images are stored at the full resolution of approximately 5 km at the subsatellite point. The pixel brightness values are calibrated by the Japan Meteorological Agency so that the corresponding brightness temperatures can be found from a fixed look-up table. GOES and Meteosat imagery in McIDAS area format are obtained from the University of Wisconsin in near real time with reduced resolution and are used in the form in which they are received; there is no additional modification to either navigation or calibration. The satellite data are averaged onto the model grid by first obtaining the image coordinates of the corners of the grid from the appropriate satellite navigation algorithm, then (assuming these to be joined by straight lines2 for computational ease) the image is scanned a line at a time and each pixel is assigned to a specific grid square. A histogram of brightness values is formed for each grid box in the validation region and used to calculate a range of statistics, including the grid-box average brightness temperature, and archived for further analysis. The off-line code used to calculate the narrowband brightness temperature is derived from the two IR window bands of the Fels–Schwarzkopf radiation code (Schwarzkopf and Fels 1991), which span the spectral region 800–990 cm21 and includes absorption due to the water vapor continuum and some water vapor lines. The spectral response function for the GMS-4 satellite sensor was digitized in steps of 10 cm21 over the range 805–985 cm21 and folded into the integration over the band that accounts for the spectral variation of the Planck function. The resulting window radiance code was compared with clear-sky line-by-line single profile reference irradiance calculations (incorporating the

1 The model Gaussian latitudes were used in calculating the grid boxes used in the validation scheme even though they are only approximately equispaced. 2 The lines are actually curves so that this assumption typically misassigns a sector of pixels at most one or two deep to a grid box adjacent to the correct one. Since this is a small proportion of the pixels in a grid box, it is considered to be sufficiently accurate.

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GMS-4 spectral response function), which indicated that the errors were less than 1 K in brightness temperature. The global modeled brightness temperatures are generated twice a day at the completion of the operational assimilation and prediction suite and use the model cloud amounts and heights (archived directly on the model physics grid) and temperature, water vapor, and surface pressure fields (archived in spectral form). The spectral response functions for GOES and Meteosat are assumed to be the same as that for GMS, and the satellite zenith angle is chosen to be zero so that a single global calculation can be used for all three satellites. This introduces a cold bias of up to 1–2 K concentrated on the eastern and western boundaries of each satellite domain. The comparisons to be described in later sections indicate that the errors due to these assumptions, which should show up as systematic variations in the comparison with satellite data, are dwarfed by the errors due to the deficiencies in the modeled cloud fields. c. Extension to cloud height validation 1) THE

CLEAR-SKY ALGORITHM

There are a number of limitations in the use of the 11-mm brightness temperature as a means of validating a model, chiefly stemming from the fact that it is mainly sensitive to the topmost radiating surface at any point with particular emphasis on higher colder clouds. Hence, in any given grid square the brightness temperature is derived from a combination of the radiative fluxes from cloud radiating from all the levels where clouds are present, but the information about this distribution of pixel brightness values is lost in the process of averaging onto the model grid scale. Thus, on the grid scale it is impossible to determine if the satellite results are due, for example, to an average over a combination of some high and some low clouds or just over an equivalent amount of midlevel clouds. To usefully analyze the dependence of the errors in model-derived brightness temperature with respect to deficiencies in simulated clouds, some measure of the subgrid-scale distribution of pixel brightness values is necessary. To this end the brightness temperature validation described in the previous section has been augmented by a simple scheme to derive cloud information from the satellite data that can be qualitatively compared with the cloud fields simulated by the model. This scheme consists of two parts. The first uses a simple cloud identification scheme based on the IR part of the ISCCP scheme (Rossow and Garder 1993a) to determine the clear-sky fraction in each grid box, while the second is an attempt to stratify clouds into pseudoheight classes. Only the IR image is used since the ultimate aim is to verify model cloud across the full diurnal cycle. Although the visible imagery could have been used when available, it was considered that to have done so would have inevitably skewed the results and unnecessarily complicated their

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FIG. 3. Monthly average of the total cloud field diagnosed from the model analyses for July 1994 (upper) compared with the ISCCP data (lower) for the average for July over the 1983–87 period.

interpretation. In the absence of extra cloud information from other spectral bands the pseudocloud fields can only serve as a tool to aid interpretation and should not be expected to correctly characterize the real clouds, particularly they are optically thin. The cloud clearing algorithm (Rikus and Kepert 1992) first identifies the pixels that are more than 4 K

colder than the spatial average over the surrounding 41 3 41 pixel square as ‘‘cloudy’’ and the remainder as ‘‘undecided.’’ The pixel values are then compared with their values on the preceding and following days. If a pixel is within 1.8 K of either of these it is ‘‘clear’’; otherwise it is ‘‘not clear.’’ If the pixel is more than 5 K cooler than the greater of the values for adjacent days

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FIG. 4. Decision table for the combination of the spatial and temporal variability tests to determine in which of the four categories (clear, cloudy, mixed, and undecided) a pixel belongs.

it is ‘‘cloudy’’; otherwise it is ‘‘not cloudy.’’ Note that a pixel can satisfy both criteria. The results of the spatial temperature and the temporal variance tests are combined according to the logic table shown in Fig. 4. The temperature thresholds and the size of the spatial-averaging domain are empirical constants chosen after careful comparison with a subjective analysis of a number of GMS images chosen to be representative of several meterological regimes. They were not varied to suit the different conditions and resolutions of the GOES or Meteosat domains and for which they may be less than optimal. Unlike the more sophisticated ISCCP tests (Rossow and Garder 1993a), the thresholds are the same for all surface types and lie in between those for land or ocean in the ISCCP scheme and hence on average should diagnose less cloud over ocean but more cloud over land. An indication of the performance of the cloud clearing algorithm as well as the satellite calibration used in the McIDAS software can be obtained by comparing the clear-sky brightness temperatures calculated from the model thermodynamic fields (obtained by setting all cloud fractions to zero) and the corresponding brightness temperature obtained from averaging pixels identified as clear of cloud for each satellite. The annual area-averaged values are given in Tables 2, 3, and 4. Although the value for the GMS domain appears to be excellent, it actually represents a compensation between TABLE 2. Domain average of brightness temperature and various cloud fields for the operational model analyses (Anal) compared with the satellite-derived values (GMS) averaged onto the model grid for the period May 1994–April 1995. The PHan values represent the results of an off-line calculation using a temperature-dependent cirrus emissivity with the Anal fields. The high cloud in this case is the product of high cloud fraction and emissivity. The ISCCP values have been averaged over the same domain but are an average for July 1983–September 1988. The GMS total cloud was derived in two ways: a sum over the derived pseudoheight cloud classes and from the cloud-clearing algorithm (in parentheses).

Brightness temperature Clear-sky brightness temperature High clouds H1M clouds Total clouds

Anal

PHan

268.2 288.1 29 37 68

274.2 275.8 — 287.9 19* 13 — 44 — 62 (77)

TABLE 3. Domain average of brightness temperature and various cloud fields for the model (Anal) compared with the satellite-derived values (GOES) averaged onto the model grid for the period May 1994–April 1995. The PHan values represent the results of an offline calculation using a temperature-dependent cirrus emissivity with the Anal fields. The effective high cloud in this case is the product of high cloud fraction and emissivity. The ISCCP values have been averaged over the same domain but are an average for July 1983 to September 1988. The GOES total cloud was derived in two ways: a sum over the derived pseudoheight cloud classes and from the cloud clearing algorithm (in parentheses).


Anal

PHan

GOES ISCCP

272.1 289.6 26 33 54

278.0 275.9 — 288.1 16* 13 — 46 — 65 (77)

— — 18 34 65

warm limb effects and cold regions associated with land surfaces, as can be seen from Fig. 5. Over the ocean the model’s surface temperature is fixed to the SST field analyzed for the previous week and consistently shows differences less than 2 K over the center of the domain along with obvious limb effects associated with larger positive differences toward the edges of the domain. This limb effect can be attributed to two main causes. First, the model brightness temperatures are derived assuming a viewing angle of zero, that is, directly above the grid point. While this is correct for the grid point directly below the satellite, for points further away the model generates smaller optical depths than those actually seen by the satellite and hence warmer brightness temperatures. Second, the area of the pixels increases toward the limb of the satellite image resulting in greater cloud contamination and hence leading to colder average brightness temperatures. Errors due to the fact that the real ocean surface is not a blackbody radiator could contribute up to 0.7 K in areas away from the Tropics (Rossow and Garder 1993b), but this is a minor effect by comparison. Over land the model’s surface temperature is diagnosed instantaneously as a function of its surface energy TABLE 4. Domain average of brightness temperature and various cloud fields for the model (Anal) compared with the satellite-derived values (Meteosat) averaged onto the model grid for the period May 1994–April 1995. The PHan values represent the results of an offline calculation using a temperature-dependent cirrus emissivity with the Anal fields. The effective high cloud in this case is the product of high cloud fraction and emissivity. The ISCCP values have been averaged over the same domain but are an average for July 1983– September 1988. The Meteosat total cloud was derived in two ways: a sum over the derived pseudoheight cloud classes and from the cloud clearing algorithm (in parentheses).

GMS ISCCP — — 25 42 65

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Anal

PHan Meteosat ISCCP

272.1 289.0 25 38 49

278.1 276.7 — 281.1 16* 9 — 38 — 44 (61)

— — 18 33 59

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FIG. 5. The annual average of the difference between the model’s surface temperature field and the brightness temperature from pixels designated as clear sky by the cloud validation scheme. Areas where the model is colder than 22 K relative to the satellite are hatched and areas where the model is warmer by more than 2 K are stippled. The contour interval is 4 K.

budget and hence should not be as realistic as the SST. In fact, most of the land areas show negative differences, implying the model underestimates the radiative temperature of the surface, with the largest magnitude differences over high terrain and for summer months. Since cloud contamination or surface emissivity effects in the satellite data should result in differences of the opposite sign, there must be another explanation for these results. The major source of the error in the model’s clear-sky brightness temperatures is error in its surface temperature. One factor that probably contributes to the model bias is the slight time mismatch between the model field that is valid for 2300 UTC and the satellite scan that ends at about 2330 UTC. This corresponds to local morning when the land is warming, and so if the satellite senses a surface temperature later than the time of the model’s calculation there will appear to be a consistent cold bias in the model field relative to the satellite. This effect is amplified because the model appears to overestimate the amplitude of the diurnal cycle with surface temperatures that are too hot during the day and too cold at night. One problem that could have complicated the analysis of the GOES region was that GOES-7 was moved from its position over 1108W to about 1358W over the period from December to February, resulting in a severe decrease in the number of satellite pixels per model grid square on the eastern boundary of the domain. This was accompanied by a more pronounced limb darkening over the Americas over the last four months of the validation period, resulting in a local apparent warm bias

of up to 5 K in brightness temperature and a consequent artificial increase in the cloud amount diagnosed from the satellite data of about 0.15 in total cloud fraction there. These extra biases do not affect the gross comparisons between the model and satellite data, although they contribute a bias of about 22 K in the overall domain area-average brightness temperature after February. In the Meteosat domain the annual area average difference is about 7 K (Table 4), which appears to be an overall bias of about 4 K together with large warm biases over central Africa that could be due to deficiencies in the surface scheme of the model or to the lack of a realistic emissivity for the Sahara. The satellite clear-sky brightness temperature is much colder than the model (and other two satellites), and the total cloud amount is low by comparison with both the ISCCP climatology and the other satellite domains. This suggests the main cause of the apparent bias is cloud contamination in the pixels identified as clear by the cloud clearing algorithm, with some contribution due to errors in the table used to convert satellite brightness values to brightness temperatures. The cloud-clearing algorithm appears to work adequately for the GOES data, which is at similar resolution to the Meteosat data, but it is possible that the problem is an artifact of the process that reduces the resolution of the image before it is received by the Bureau of Meteorology. 2) SEPARATION

INTO PSEUDOHEIGHT CLOUD

CLASSES

The second part of the scheme partitions the satellite brightness temperatures into classes that are representative of the model’s height classes under the assumption that all cloud behaves as a pure blackbody radiator in thermal equilibrium with the surrounding atmosphere. Then direct comparison of satellite brightness temperature with the model’s temperature field at each grid point produces an equivalent sigma level for the cloud. In evaluating the cloud height class, the histogram of brightness temperatures for each grid box is used to estimate the cloud amount of each type (i.e., ultralow, low, middle, high, or ultrahigh)3 as defined by the cloud height limits of the model that separate the layers shown for the ‘‘original’’ model in Fig. 1. A serious shortcoming of this type of classification scheme is that it is incapable of distinguishing between middle clouds and optically thin cirrus. An added complication is that the cloud brightness temperature depends on the product of cloud amount and emissivity so that the cloud fractions derived from the satellite in this way are only effective cloud amounts. For both these reasons it is not possible

3 Ultralow cloud is cloud below the bottom of the low cloud layer. Ultrahigh is cloud above the top of the high cloud layer.

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to compare the magnitudes of the model high cloud amounts directly with those derived from the satellite data, although the distributions can be used to derive useful diagnostics. To avoid the problems associated with cloud overlap, the model’s high and middle clouds were combined using the same random overlap assumption as the model and compared with the sum of high and middle satellite pseudoclouds. While this has the advantage of accounting for the misplaced cirrus, the combined amount can still only be considered as an effective cloud fraction due to the presence of nonblack middle clouds. 4. Comparison of results derived from the satellites with the model a. The GMS domain 1) BRIGHTNESS

TEMPERATURE

The geographical distribution of the annual average brightness temperature over the GMS region from the satellite is shown in Fig. 6a, and the corresponding bias of those obtained from model analyses are shown in Fig. 6b. The most obvious feature is the predominance of biases colder than 26 K over most of the domain, which reflects the area average bias of 27.6 K (see Table 2). The overall cold bias is an artifact of the assumption that cirrus interacts with thermal radiation as a blackbody, and this complicates the comparison between modeled and GMS cloud fields. To counter this effect, a second series of off-line4 calculations were performed using the temperature-dependent cirrus emissivity parameterization due to Platt and Harshvardhan (1990) for high cloud. The area-averaged model brightness temperature bias with these more realistic emissivities (shown as PHan in Table 2) is improved to 21.6 K. The broadscale patterns are shown in Fig. 6c. The midlatitude patterns are quite good, although there is a distinct cold bias characterized by an equatorward shift of the colder contours. The warm subsidence region over Australia is too cold, although there is a suggestion of the warm area off the northwest coast. The structure in the model Tropics is fragmented with less coherence than the satellite data and a number of the colder regions are in the wrong position. For example, the model simulates a region of colder brightness temperatures over New Guinea itself, whereas the corresponding region in the satellite field is to the north. This may be attributable to problems with the diffusion of moisture along sigma levels in the area of sharp topography or possible systematic errors in the PBL parameterization. The model does show a cold region north of New Guinea, but it

4 This ignores any feedback from the effects of the revised cloud optical properties on the model’s thermodynamic fields during the assimilation cycle.

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appears to be anomalous and unconnected with the convection over New Guinea. The corresponding bias distribution (Fig. 6d) shows that the modeled fields are in better agreement with the GMS field, with differences less than 6 K over most of the domain and the larger cold biases now restricted to the Australian continent, parts of the ITCZ, and the Northern Hemisphere subtropical ridge region over the Pacific. Over Asia and the Indonesian and New Guinea regions there are areas that show alternating patterns of positive and negative biases where the model has difficulty in characterizing the structure of the large-scale cloud fields. There is a notable region of strong warm bias south of the equator at the date line, with a narrow tongue extending west toward New Guinea, which shows that the model fields completely miss the signature due to convection over the warm pool. There is another region of warm bias on the western boundary of the domain extending from the Tropics to 208N related to the model’s lack of strong convection over India and the Bay of Bengal. 2) ZONAL

AVERAGE BRIGHTNESS TEMPERATURE AND CLOUD FIELDS

Figure 7 shows the annual zonal means of the brightness temperatures, high cloud fraction, and high plus middle cloud fraction (H1M) for the GMS data, as well as the results calculated from the model analysis using blackbody (Anal) and gray (PHan) high clouds. The blackbody results have a cold bias of up to 10 K in the Tropics and underestimate the peaks in the subtropical ridges by about 8 K. The gray cirrus results moderate the cold bias substantially even to the extent of producing a moderate warm bias in the southern Tropics. In both cases the model shows a tropical minimum only in the Northern Hemisphere, while the satellite shows a double valley straddling the equator. This asymmetry in the model (due principally to the lack of convection over the warm pool south of the equator) is even more obvious in the zonal average high (Fig. 7b) and H1M (Fig. 7c) cloud fields. Apart from a disparity in magnitude, the model zonal high cloud shows a similar pattern to that for the satellite in the Northern Hemisphere but is substantially different in the Southern Hemisphere. The zonal effective high cloud amount (PHan) derived by multiplying the high cloud fraction by the Platt–Harshvardhan cirrus emissivity is in better agreement with the satellite, although it still suggests a model overestimate almost everywhere. Note the flattening of the gray cirrus curve relative to the blackbody cirrus toward the poles, which is an artifact of the temperature dependence of the emissivity parameterization. The overall shape of the zonally averaged H1M fields matches the satellite results quite well apart from the tropical asymmetry and an overall magnitude difference of about 0.1. The monthly zonal averages for the model analyses

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FIG. 6. The brightness temperatures for the GMS validation domain-averaged over the year from April 1994 to March 1995 derived from the GMS-4 data (a) and the corresponding bias in the brightness temperatures calculated from the model’s analyses (b) assuming blackbody cirrus. The results using a more realistic parameterization for cirrus emissivity are shown in (c), with the corresponding bias in (d). The biases are obtained by subtracting the GMS brightness temperature from the model-derived fields. Biases below 26 K are hatched and those above 6 K are stippled. The contour interval is 4 K.

and GMS-4 brightness temperatures (Fig. 8) show how the areas of agreement and disagreement in the annual mean are built up from a number of different components. The double-valley structure in the GMS data in the Tropics is clearly a superposition of a Northern Hemisphere ITCZ in May to September and a Southern Hemisphere ITCZ in November to March. The model misses the double structure because it has a strong southern contribution to the ITCZ only in the December to February period, and even then it is too broad with too much cold cloudiness to the north of the equator.

The tropical brightness temperatures based on blackbody cirrus are systematically too cold in the model analyses for the entire year except for the region around 108S, which shows agreement in both slope and magnitude in a number of months, particularly the November to January period. Overall, the model features tend to be more zonal than the satellite patterns, and this is reflected in the narrower peaks and valleys in the zonal average. The strength of the model ITCZ is weakest in April, in agreement with the GMS results. However, the quiescent period during October and November is not

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FIG. 7. Zonal annual means of (a) brightness temperature, (b) high cloud, and (c) the sum of high and middle cloud fractions over the GMS domain shown in Fig. 6. The brightness temperatures are calculated with blackbody cirrus (Anal) and with the temperature-dependent emissivity of Platt and Harshvardhan (PHan). The PHan high cloud is the product of cloud amount and emissivity.

well simulated by the model. In general the model analyses overestimate the strength of convection over the Indonesian region, resulting in a large overestimation of the strength of the ITCZ, particularly in September. The strongest ITCZ activity in the GMS data is in December and January, and this is well reproduced by the model apart from a tendency to overestimate the strength north of the equator. The satellite shows markedly more variation in ITCZ strength over the year than the model. In the subsidence regions of the subtropical ridges, the cold bias is clearly evident, with a suggestion of a slightly better fit in the winter. In the Southern Hemisphere the latitudes from 108 to 408S are dominated by the strong cold bias of the Australian continent, which appears to be due to excessively cold surface temperatures and excessive cloud. The calculations using the gray cirrus approximation (PHan) generally improve the fit to the satellite brightness temperatures, particularly in the Tropics, although the strong convective period during the southern summer is not so well simulated. The subsidence region peaks are better than in the black cirrus case but are

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generally still badly underestimated, suggesting that the model is simulating too much cloudiness there. The agreement poleward of 208 in both hemispheres is vastly improved for most months, which emphasizes the need for gray cirrus optical properties in the midlatitudes. The monthly zonal averages of the differences between the clouds diagnosed from the model’s analysis and the satellite pseudoclouds are shown in Fig. 9. The total cloud amounts can be derived from the satellite in two ways. The first is to use the fraction of the pixels in the grid square not binned as ‘‘clear’’ (see Fig. 4), which is expected to be an overestimate because it includes pixels that are ‘‘mixed’’ and ‘‘undecided.’’ Alternatively, the total cloud field can be defined as the sum of the pseudocloud fields. The fields defined in these ways are very similar in geographical distribution and differ mainly in magnitude, so the total cloud errors were calculated using the average of the two methods. The difficulty in comparing the blackbody model high clouds with the transparent high clouds seen by the satellite means that the absolute difference between the two must also be considered as an arbitrary quantity, suggesting that only relative latitudinal trends should be analyzed. However, differences below 0.1 strongly suggest an underestimation in model high clouds, and values above 0.2 almost certainly mean an overestimate in the latitudes away from the regions corresponding to the subtropical ridges. Hence, it is possible to conclude that the cloud fraction south of 308S is overestimated by the model in almost every month, which is consistent with the cold bias in brightness temperature there. The failure of the model analyses to reproduce the southerly march of the ITCZ in November and December and keep it south of the equator until April is indicated by the fairly ubiquitous peak in all three cloud differences that appear between the equator and 158N. The model’s difficulty in reproducing the brightness temperatures north of 308N in June and July seems to be due to anomalous structure in the model’s high cloudiness exemplified by the strong difference peaks. In August the flatness of the high cloud difference in that region suggests that the disagreement appears to be due more to variations in H1M and total cloudiness. The cold bias in the peak between 108 and 308S is often associated with small values of the high cloud error and peaks in either H1M or total cloudiness, indicating problems with the diagnosis of low and middle clouds in the southern subsidence region. In the last three months of the validation period there is a peak in the high cloud error in this region, which has a strong influence on the shape of the brightness curve, but the overall bias still seems to be strongly related to the errors in total and H1M clouds. The geographical distributions of monthly averaged H1M differences for January and July are shown in Fig. 10 and illustrate a number of features common to

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FIG. 8. The monthly zonal averages for brightness temperature over the GMS domain from May 1994 (top left) to April 1995 (bottom right). The thick line (Anal) corresponds to the model calculations using blackbody cirrus and the thin line (PHan) to calculations using temperature-dependent cirrus emissivity. The marked line corresponds to the GMS IR brightness temperatures averaged onto the model grid.

the entire validation period. The model systematically underestimates clouds over the northwestern corner of the domain, the tropical region south of the equator near the date line, and the eastern Indian Ocean. The underestimate in the Southern Hemisphere is masked in the zonal averages for some months by the overestimation of cloud over the Australian continent. In November through February the GASP analyses underestimate the H1M cloud fraction associated with the Australian monsoon and this can be seen in the January mean field. The severe warm bias in brightness temperature associated with a lack of clouds in the model marking the southern half of the strong convection region over the oceanic warm pool near the date line on the eastern boundary of the domain is exacerbated in December, and although still present in January the bias in H1M clouds is not as large. In July the overestimate in H1M over the Australian continent extends northward over the maritime continent region, where the model consequently simulates brightness temperatures that are too cold. This is masked in the zonal averages for July by the large underestimate of H1M (and corresponding warmer

brightness temperatures) over the Tropics over the entire eastern half of the domain. The monthly zonal averages of total cloud differences show that the clouds in the higher northern latitudes are always underestimated by the model, although some of this may be attributed to the difficulties the satellite cloud clearing algorithm has with snowcovered land. Apart from this there is an interesting systematic variation in the monthly cloudiness south of 308N. Initially, in May there are two peaks at about 158N and 108S due to the model’s overestimate of low clouds in the subtropical ridge regions accompanied by too much cloudiness in the Southern Hemisphere overall. These extremes moderate over the next few months and the Southern Hemisphere cloudiness decreases until September when the model has developed a systematic underestimation south of 108S. Over the next two months the peak in the Northern Hemisphere grows substantially with a maximum in November. This behavior in total cloud is essentially decoupled from the high and H1M fields and hence indicates a systematic error in the model’s low clouds in the subtropical ridge regions, particularly in the Northern Hemisphere.

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FIG. 9. The monthly zonal averages for the differences between the model- and satellite-derived high clouds (thin line), high plus middle clouds (thin marked line), and total cloud amounts (thick line) over the GMS domain. The model fields have been added using the random overlap assumption. The total cloud derived from the satellite is the average of the nonclear pixels from the cloud-clearing algorithm and the sum of all the pseudocloud fields generated from a comparison of brightness temperature histograms with temperature profiles from the model.

FIG. 10. The monthly mean differences between the high plus middle cloud fields from the model and those derived from the satellite data for January (a) and July (b). The contour interval is 0.1 in cloud fraction and the zero contour is suppressed. Differences below 20.2 are hatched, while those above 0.2 are stippled.

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FIG. 11. The brightness temperatures for the GOES validation domain averaged over the year from April 1994 to March 1995 derived from the GOES-7 data (a) and the model’s analyses with temperature-dependent cirrus emissivity (c). The corresponding biases obtained by subtracting the GOES field from the model field are shown in (d), while those derived assuming blackbody cirrus are shown in (b). Biases below 26 K are hatched and those above 6 K are stippled. The contour interval is 4 K.

b. The GOES and Meteosat domains 1) BRIGHTNESS

TEMPERATURE

The annual mean brightness temperatures and biases for the GOES and Meteosat validation domains are shown in Figs. 11 and 12, respectively, with the corresponding area annual averages in Tables 3 and 4. In both domains the ITCZ lacks the zonal connected structure of the satellite data. As for the GMS case, more insight into the underlying cloud patterns can be gained by introducing realistic cirrus emissivities

that result in a warm overall bias of around 1–2 K. As can be seen from the bias distributions (Figs. 11d and 12d) the strong cold biases over the oceans are reduced, but the warm biases due to the failure to simulate the strong convection over equatorial and southern land masses or the ITCZ are exacerbated. Although the model has a suggestion of the correct pattern over South America, it fails to simulate the strength of convection, particularly in the interior of the continent. The warm bias patch in the central southern Atlantic does not change (except to become

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FIG. 12. The brightness temperatures for the Meteosat validation domain averaged over the year from April 1994 to March 1995 derived from the Meteosat data (a) and the model’s analyses with temperature-dependent cirrus emissivity (c). The corresponding biases obtained by subtracting the Meteosat field from the model field are shown in (d), while those derived assuming blackbody cirrus are shown in (b). Biases below 26 K are hatched and those above 6 K are stippled. The contour interval is 4 K.

broader in extent) between the two calculations with different cirrus emissivity because there are no high clouds over that region for most of the year. The contiguous area of positive bias in the North and South Atlantic emanating from South America suggest that the model’s inability to represent the ITCZ over the northern equatorial Atlantic and the southwest Atlantic convergence zone (SWACZ) in the southwest corner of the Meteosat domain are strongly related to its inability to simulate the strength of the convection

over the Amazon Basin. Note the moderately warm biases off the west coast of South America presumably due to the model diagnosing insufficient oceanic stratocumulus, which indicates that the static stability–dependent cloud parameterization is not adequate in this region. A comparison of the total cloud derived from the Meteosat data and the ISCCP climatology suggests that the cloud-clearing algorithm overestimates the amount of clear sky substantially in contrast to the results for the other two satellites.

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FIG. 13. Zonal annual means of (a) brightness temperature, (b) high cloud, and (c) the sum of high and middle cloud fractions over the GOES domain shown in Fig. 11. The brightness temperatures are calculated with blackbody cirrus (Anal) and with the temperaturedependent emissivity of Platt and Harshvardhan (PHan). The PHan high cloud is the product of cloud amount and emissivity.

2) ZONAL

AVERAGE BRIGHTNESS TEMPERATURE AND CLOUD FIELDS

Figures 13 and 14 show the zonal annual average of brightness temperature, high clouds, and H1M clouds for the GOES and Meteosat domains, respectively. The model brightness temperatures calculated using black cirrus do not represent well the shape of the peaks corresponding to the subsidence regions and show a marked asymmetry between the hemispheres. The main difference between the model results in the two domains is associated with the structure of the ITCZ. The broad character of the ITCZ in the Meteosat domain is quite well represented by the model, particularly in the transparent cirrus case, which is a consequence of the fact that the overall shape of the zonal average cloud is generally very good. In contrast, even with black cirrus the model is unable to reproduce the magnitude and sharpness of the valley corresponding to the ITCZ in the GOES region, a failure obviously associated with its underestimation of tropical high and H1M clouds

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FIG. 14. Zonal annual means of (a) brightness temperature, (b) high cloud, and (c) the sum of high and middle cloud fractions over the Meteosat domain shown in Fig. 12. The brightness temperatures are calculated using blackbody cirrus (Anal) and with the temperaturedependent emissivity of Platt and Harshvardhan (PHan). The PHan high cloud is the product of cloud amount and emissivity.

there. The zonal averages for brightness temperature for each month for the GOES domain shown in Fig. 15 show that the model calculation using black cirrus (Anal) actually simulates the strength of the ITCZ quite well in the July–October period but overestimates it by about 10 K in both May and April. During the quiescent months from December to March the model does not seem to be capable of representing an ITCZ at all— behavior that is clearly correlated with a dearth of high clouds in the ITCZ latitude band shown by the corresponding zonal cloud errors in Fig. 16. This feature is also evident in the H1M and total cloud error fields, suggesting that the model does not adequately simulate the cloud structure of the ITCZ in this domain. The introduction of transparent cirrus (PHan) yields substantially less agreement with the GOES data in the Tropics but tends to improve the simulation of the rest of the domain. This is in direct contrast with the Meteosat domain (Fig. 17), where the use of transparent cirrus results in excellent agreement for the magnitude of the valley corresponding to the ITCZ for a number of months; particularly the June to August period when

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FIG. 15. The monthly zonal averages for brightness temperature over the GOES domain from May 1994 (top left) to April 1995 (bottom right). Anal corresponds to calculations using blackbody cirrus, while PHan corresponds to temperature-dependent emissivity calculations. The thin marked line corresponds to GOES IR brightness temperatures averaged onto the model grid.

the major convection is mainly restricted to equatorial Africa. In this domain the values calculated assuming black cirrus fit the ITCZ of the satellite data very well in September–November but are poor in March and April. The southerly bias in the position of the ITCZ at that time is also evident in the plots for cloud errors (Fig. 18).5 The patterns in the regions of the subsidence zones in each hemisphere are also consistent with the model’s tendency to overestimate the geographical extent of the cloud-sparse subsidence regions. The inability of the model to capture the magnitude and detailed shape of the Northern Hemisphere peaks, particularly in the period from June to August, is due to a lack of strong convection over the eastern United States, Central America, and northern and central Africa. Again this is apparent in the high cloud errors that show that there is an associated underestimate of cloud. The strong warm bias in the GOES domain in the region of the

5 Note that the sharp change at the poleward limits of the cloud errors for the Meteosat domain are artifacts of the cloud clearing algorithm caused by the proximity of the edges of the satellite data.

southern subtropical ridge that generally shows up as a peak in brightness temperature at around 108S is predominately due to strong warm biases over South America associated with the southerly movement of the area of strong convection. It is clearly associated with the underestimate of high clouds diagnosed there in the September–December period and again in April and is accompanied by a pronounced negative difference in H1M and total cloudiness extending further to the south. This underestimate of total and H1M clouds is exacerbated in the period from September to April because the model fails to simulate the strength of the SPCZ adequately. This monthly variation in total cloud is part of an annual cycle that is very similar to that found for the GMS region that it lags in phase by about one month. In the GOES case the three cloud field errors show a greater correlation with each other than was apparent for the GMS cloud, indicating systematic error behavior across all three cloud levels. This annual cycle in total cloudiness is also present for the Meteosat data but is suppressed in the north, possibly because the subsidence zone lies mainly over land. The peak for the Southern Hemisphere subtropical ridge zone in the Meteosat domain is dominated by the strong warm biases

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FIG. 16. The monthly zonal averages for the differences between the model- and satellite-derived high clouds (thin line), high plus middle clouds (thin marked line), and total cloud amounts (thick line) over the GOES domain. See Fig. 9 for details.

due to a lack of simulated convection over southern Africa from September through to January and by the lack of strong convection over South America from October through to January. The biases arising from the repositioning of GOES-7 during the December–February period are present at all latitudes and hence result in only small, mainly uniform shifts in the magnitude of the zonal averages in the last four months of the validation period. 5. Discussion and summary This validation study has highlighted a number of deficiencies in the model’s analysis fields. The ITCZ is in general badly simulated in the model analyses, particularly over the oceans, although this may be a symptom of data scarcity. The large-scale convection over the Pacific warm pool is too weak and shows a distinct northern bias. The convection over Indonesia is patchy and the model simulates convection over New Guinea instead of over the ocean to the north. In addition, the model seems to be unable to simulate the strength and distribution of the effects of strong convection over the continents in summer, particularly that over the Amazon Basin and Africa. In both of the latter regions there are

associated areas of anomalous cloud activity off the eastern coasts. At the same time, as it misses the strongest cold biases over South America the model generates a ring of convection along the western coast in an area that shows warm brightness temperatures in the satellite data. In all these cases the model analysis fields lack the large-scale continuity of the patterns in the satellite data. The Australian continent has a cold brightness temperature bias throughout the year, probably due to too much model cloud but which may be related to the problem with land surface temperatures identified in the comparison with clear-sky brightness temperatures. The GOES and Meteosat domains show a tendency for the model analyses to overestimate the brightness temperatures in the summer months. This is also true of the GOES domain for the Northern Hemisphere but not for the Southern Hemisphere. In the interpretation of the results it must be remembered that the IR data is mainly sensitive to upper-level cloud, which in turn depends on the details of the uppertropospheric humidity field. Unfortunately this is probably the least well known field and is poorly characterized by the model because of the lack of assimilation of moisture data in the model above 300 hPa and in data-sparse regions in general. Thus, the upper-tropo-

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FIG. 17. The monthly zonal averages for brightness temperature over the Meteosat domain from May 1994 (top left) to April 1995 (bottom right). Anal corresponds to calculations using blackbody cirrus, while PHan corresponds to temperature-dependent emissivity calculations. The thin marked line corresponds to Meteosat IR brightness temperatures averaged onto the model grid.

spheric humidity field is strongly dependent on the model’s first guess, which consists essentially of a warmrunning model with some forcing from below. The imbalance between the thermodynamical, dynamical, and hydrological structures in the analysis means that a spinup of upper-level moisture is inevitable and is reflected in the upper-level moisture fields that provide the first guess for the analysis. The spinup and the absence of even lower level moisture data over the oceans strongly influences the operation of the model’s convection parameterization scheme: it is not being triggered at the right place or time. Since the cloud fields depend on features of the moisture distribution that is predominately driven by the convection, the inherent errors in that distribution severely impact the instantaneous cloud, particularly in data-sparse regions. The operational model versions used throughout this study use blackbody cloud optical properties, which results in an overall cold bias in brightness temperatures. The use of a more realistic parameterization of high cloud emissivity as a function of cloud temperature due to Platt and Harshvardhan (1988) and based on data from layer clouds generally improves the agreement of the model brightness temperatures with the satellite, ex-

cept for the Tropics where black cirrus often represents the effects of the ITCZ better during periods of strong convective activity. The results of the off-line calculations strongly support the need to incorporate fully interactive cloud optical properties into the model, not only to better simulate the cloud–radiation interaction but also so that it can account for the relevant feedback processes. The scheme adopted here, however, needs to be modified to account for optically thick cloud in areas of strong convective activity. Quite apart from the problems inherent in the characterization of moisture in the model analysis, there are a number of obvious shortcomings in the model’s diagnostic cloud scheme that need to be addressed. Overall the high cloud amounts from the model are overestimated substantially relative to both the satellite averages and the ISCCP climatology, while the sum H1M is underestimated, particularly for the GOES domain. This suggests that overall the model underestimates midlevel cloud amounts. By the same reasoning the low cloud amounts appear to be underestimated in the GOES region but not the other two. There is a suggestion that the model overestimates the frequency of clear and overcast skies relative to the

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FIG. 18. The monthly zonal averages for the differences between the model- and satellite-derived high clouds (thin line), high plus middle clouds (thin marked line), and total cloud amounts (thick line) over the Meteosat domain. See Fig. 9 for details.

intermediate values that might be expected at the model’s resolution. As with any diagnostic scheme based on large-scale variables, there is the problem that there are not enough degrees of freedom in the current parameterization; the critical relative humidities were chosen to satisfy global cloud fraction amounts. The form of (1) then essentially fixes the distribution of cloud amounts diagnosed by the model. The use of global constants in the scheme is restrictive and does not sufficiently allow for variations in subgrid-scale distributions in different types of clouds, different meteorological conditions, and different regions. The large difference in the behavior of the diagnostic cloud scheme in the three different regions stresses the need for global satellite data intercomparisons as done here. Tuning a parameterization for one region of the globe is not sufficient; global fields require global validation. Ultimately, improvement must come via improvements to the model’s hydrological cycle and moisture assimilation, including more sophisticated methods of obtaining clouds and humidity data from areas that are currently not well sampled. Without these, implementation of a more sophisticated cloud parameterization, including interactive cloud optical properties, is unlikely to significantly improve the simulated cloud fields. Some of

these concerns may be amenable to solution with a prognostic scheme, but this remains to be proven. There are a number of avenues for possible improvement in the validation scheme itself. The results from ISCCP and the availability of data from a number of geostationary satellites suggest that a simple global scheme could be implemented. The insensitivity of the 11-mm brightness temperature to mid- and low-level clouds suggests a need for other spectral intervals to be considered, and here the next series of geostationary satellites will provide a fairly wide choice. Currently there is the option of adding the visible channel (although this would be at the cost of restricting the validation to daytime) or the water vapor channel (6.3 mm), but this has only recently become available on GMS-5. The addition of the former would enable the full ISCCP cloud scheme to be implemented, which would improve the height identification as well as provide some validation for the model’s shortwave cloud optical properties. The addition of the water vapor channel and an additional window channel to the validation scheme should yield more useful cloud temperature information that could be used to better partition the cloud brightness temperatures into cloud height classes.

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6. Conclusions The BMRC cloud validation scheme allows ongoing validation of some aspects of the cloud–radiation interaction in the Australian Bureau of Meteorology’s operational NWP models on a day-to-day basis and has been in operation since late 1991. During this time the assimilation system and model have undergone a number of evolutionary changes, including increases in vertical and horizontal resolution. For the purposes of this study only the results from a year in which the model configuration was stable were considered. The scheme has demonstrated that although the comparison of the model’s cloud fields with climatology may be considered adequate (depending on the climatological dataset chosen), real-time cloud fields are more difficult to characterize and validate, particularly if cloud optical properties are also involved. The infrared satellite data scheme has proven to be a good qualitative validation for the actual cloud patterns, at least on broad scale, but unfortunately is very sensitive only to high cloud. The scheme was modified to allow for temperature-dependent cirrus emissivity for high clouds, which improved the overall biases but obviously not the patterns. To aid in the interpretation of the results, a cloud clearing algorithm was applied to generate clear-sky fractions from the satellite data, although the results can only be considered qualitative due to the limitations of single-channel data. In addition, the brightness temperatures were divided into brightness temperature ranges that allow qualitative comparison with the model’s cloud fields using the assumption of blackbody cloud. Although the brightness temperatures are not very sensitive to low cloud, the combination of clear-sky fraction and high and middle cloud amounts allow some inferences to be made about the low cloud in the model. Using a combination of these diagnostics it is possible to characterize the shortcomings of the diagnostic cloud scheme and their relationships to problems in the model’s simulation of the hydrological cycle and analysis of moisture fields. The implications of the results of the validation scheme to date are that the upper-level humidity field is very poorly characterized by the model and the data assimilation scheme and does not contain sufficiently accurate information about the distribution of cloud. The main requirement for improved analyses is more observational moisture data above 300 hPa. There is a suggestion that the lower-level humidity fields are incompatible with the parameterization of convection and shallow convection in the model, which is probably related to insufficient data as well as indicating a need to improve the parameterization. The availability of the satellite data and the disagreement with the brightness temperatures calculated from the model’s analysis fields strongly suggests that the cloud information inherent in the satellite imagery should be included directly into

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the assimilation cycle, possibly in the form of diabatic initialization or through bogus moisture data. Although the cloud validation scheme is simple, it has been shown to be capable of providing useful and timely information about the operational medium-range prediction model and has also been used as validation tool for experimental prototypes of the model. It will soon be applied to the bureau’s operational regional models as well. A cloud forecast scheme based on the model predictions in the medium range and utilizing the brightness temperature algorithms has been operational for some months and has proven to be a useful tool to operational forecasters. Acknowledgments. I want to thank J. Kepert for writing the original programs for processing the satellite data and N. Davidson, K. Puri, W. Bourke, and the other members of the Medium Range Prediction Group at BMRC for their comments on the various stages of this paper. REFERENCES Alexander, R. C., and R. L. Mobley, 1976: Monthly average seasurface temperatures and ice-pack limits on a 18 global grid. Mon. Wea. Rev., 104, 143–148. Anthes, R. A., 1977: A cumulus parameterization scheme utilizing a one-dimensional cloud model. Mon. Wea. Rev., 105, 270–286. Bourke, W., T. Hart, P. Steinle, R. Seaman, G. Embery, M. Naughton, and L. Rikus, 1995: Evolution of the Bureau of Meteorology Global Assimilation and Prediction system. Part 2: Resolution enhancements and case studies. Aust. Meteor. Mag., 44, 19–40. Cess, R. D., and Coauthors, 1990: Intercomparison and interpretation of climate feedback processes in seventeen atmospheric general circulation models. J. Geophys. Res., 95, 16 601–16 615. Ellingson, R. G., J. Ellis, and S. B. Fels, 1991: The intercomparison of radiation codes in climate models (ICRCCM): Longwave results. J. Geophys. Res., 96, 8929–8953. Fels, S. B., and M. D. Schwarzkopf, 1975: The simplified exchange approximation: A new method for radiative transfer calculations. J. Atmos. Sci., 32, 1475. Geleyn, J.-F. 1981: Some diagnostics of the cloud-radiation interaction in the ECMWF forecasting model. Proc. Workshop on Radiation and Cloud-Radiation Interaction in Numerical Modelling, Reading, United Kingdom, ECMWF, 135–162. Hart, T. L., W. Bourke, B. J. McAvaney, B. W. Forgan, and J. L. McGregor, 1990; Atmospheric general circulation simulations with the BMRC global sprectral model: The impact of revised physical parameterizations. J. Climate, 3, 436–459. Kuo, H. L., 1974: Further studies of the parameterization of the influence of cumulus convection on large-scale flow. J. Atmos. Sci., 31, 1232–1240. Lacis, A. A., and J. E. Hansen, 1974: A parameterization for the absorption of solar radiation in the earth’s atmosphere. J. Atmos. Sci., 31, 118–133. Li, Z.-X., and H. Le Treut, 1989: Comparison of GCM results with data from operational meteorological satellites. Ocean–Air Interactions, 1, 221–237. Manabe, S., and J. L. Holloway Jr., 1971: Simulation of climate by a global general circulation. 1. Hydrological cycle and a heat balance. Mon. Wea. Rev., 99, 335–369. Mitchell, K. E., and D. C. Hahn, 1990: Objective development of diagnostic cloud forecast schemes in global and regional models. Preprints, Seventh Conf. on Atmospheric Radiation, San Francisco CA, Amer. Meteor. Soc. J138–J145.

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Morcrette, J. J., 1991: Evaluation of model-generated cloudiness: Satellite-observed and model-generated diurnal variability of brightness temperature. Mon. Wea. Rev., 119, 1205–1224. Platt, C. M. R., and Harshvardhan, 1988: Temperature dependence of cirrus extinction: Implications for climate feedback. J. Geophys. Res., 93, 11 051–11 058. Rikus, L., and T. Hart, 1988: The development and refinement of a diagnostic cloud parameterization scheme for the BMRC global model. IRS’88: Current Problems in Atmospheric Radiation, J. Lenoble and J.-F. Geleyn, Eds., A. Deepak, 116–118. , and J. Kepert 1992: Validating the cloud diagnosis scheme in the BMRC GCM. IRS’92: Current Problems in Atmospheric Radiation, S. Keevalik and O. Ka¨rner, Eds., A. Deepak, 15–18. Rossow, W., and R. A. Schiffer, 1991: ISCCP cloud data products. Bull. Amer. Meteor. Soc., 72, 2–20. , and L. C. Garder, 1993a: Cloud detection using satellite measurements of infrared and visible radiances for ISCCP. J. Climate, 6, 2341–2369. , and , 1993b: Validation of ISCCP cloud detections. J. Climate, 6, 2370–2393. Schwarzkopf, M. D., and S. B. Fels, 1991: The simplified exchange method revisited: An accurate, rapid method for computation of infrared cooling rates and fluxes. J. Geophys. Res., 96, 9075– 9096. Seaman, R., W. Bourke, P. Steinle, T. Hart, G. Embery, M. Naughton, and L. Rikus 1995: Evolution of the Bureau of Meteorology’s Global Assimilation and Prediction system. Part I: Analysis and initialisation. Aust. Meteor. Mag., 44, 1–18 Slingo, A., 1989: A GCM parameterization for the shortwave radiative properties of water clouds. J. Atmos. Sci., 46, 1419–1427.

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