Evaluation of convection‐permitting model simulations of cloud ...

PUBLICATIONS Journal of Geophysical Research: Atmospheres RESEARCH ARTICLE 10.1002/2014JD022143 Special Section: The 2011-12 Indian Ocean Field Campaign: AtmosphericOceanic Processes and MJO Initiation Key Points: • Microphysics schemes are evaluated using data collected during AMIE/DYNAMO • There is a power law relationship between echo-top height and cloud radius • Cold pool frequency is sensitive to raindrop breakup and self-collection

Correspondence to: S. Hagos, [email protected]

Citation: Hagos, S., Z. Feng, C. D. Burleyson, K.-S. S. Lim, C. N. Long, D. Wu, and G. Thompson (2014), Evaluation of convectionpermitting model simulations of cloud populations associated with the Madden-Julian Oscillation using data collected during the AMIE/DYNAMO field campaign, J. Geophys. Res. Atmos., 119, 12,052–12,068, doi:10.1002/ 2014JD022143. Received 11 JUN 2014 Accepted 15 OCT 2014 Accepted article online 21 OCT 2014 Published online 12 NOV 2014

Evaluation of convection-permitting model simulations of cloud populations associated with the MaddenJulian Oscillation using data collected during the AMIE/DYNAMO field campaign Samson Hagos1, Zhe Feng1, Casey D. Burleyson1, Kyo-Sun Sunny Lim1, Charles N. Long1, Di Wu2,3, and Greg Thompson4 1

Pacific Northwest National Laboratory, Richland, Washington, USA, 2Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA, 3Science Systems and Applications, Inc., Lanham, Maryland, USA, 4 National Center for Atmospheric Research, Boulder, Colorado, USA

Abstract Regional convection-permitting model simulations of cloud populations observed during the 2011 Atmospheric Radiation Measurement (ARM) Madden-Julian Oscillation Investigation Experiment/Dynamics of the Madden-Julian Oscillation Experiment (AMIE/DYNAMO) field campaign are evaluated against ground-based radar and ship-based observations. Sensitivity of model simulated reflectivity, surface rain rate, and cold pool statistics to variations of raindrop breakup/self-collection parameters in four state-of-the-art two-moment bulk microphysics schemes in the Weather Research and Forecasting (WRF) model is examined. The model simulations generally overestimate reflectivity from large and deep convective cells, and underestimate stratiform rain and the frequency of cold pools. In the sensitivity experiments, introduction of more aggressive raindrop breakup or decreasing the self-collection efficiency increases the cold pool occurrence frequency in all of the simulations, and slightly reduces the reflectivity and precipitation statistics bias in some schemes but has little effect on the overall mean surface precipitation. Both the radar observations and model simulations of cloud populations show an approximate power law relationship between convective echo-top height and equivalent convective cell radius.

1. Introduction Understanding and realistic representation of tropical organized convection in climate models has been an important area of research for many decades. Traditionally, convective processes are represented in regional and global climate models through parameterizations [Arakawa, 2004; Randall et al., 2003]. With the increased availability of computational resources and scalability of model dynamical cores, convection-permitting modeling with grid spacing of less than 10 km is becoming a plausible path for bypassing the need for cumulus parameterization to represent the large-scale effects of organized convection in models. The limited number of global cloud-permitting modeling studies so far has shown some promise in realistically representing important climatic processes. For example, simulations by Miura et al. [2007] using the Japanese Earth Simulator System show that their convection-permitting model produces realistic cases of the Madden-Julian Oscillation (MJO). Several models such as the NASA’s Goddard Cumulus Ensemble model and the Nonhydrostatic Icosahedral Atmospheric Model of Japanese Meteorological Agency are also being used for research applications at cloud-permitting resolutions [Hong and Dudhia, 2011]. Another computationally expensive but currently more practical approach to cloud-permitting modeling involves the use of regional models. For example, regional cloud-permitting model simulations using the Weather Research and Forecasting (WRF) model have been shown to realistically simulate observed characteristics of organized tropical convection and the MJO [Hagos and Leung, 2011; Hagos et al., 2013]. Similar model configurations have been used to examine the role of moisture-convection interaction in MJO in simulations with explicit and parameterized convection [Holloway et al., 2013]. Regional cloud permitting models are also being used operationally around the world. National and regional meteorological services such as the German Meteorological Service [e.g., Hohenegger and Schär, 2007], the Japanese Meteorological Agency [e.g., Saito et al., 2006], and UK Met Office [e.g., Lean et al., 2008] have been using such models for

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Numerical Weather Prediction applications. In addition to their application in research and forecasting in their own right, regional cloud-permitting models can also be used as relatively inexpensive testbeds for evaluation and improvement of physics parameterizations which will be required for future global cloudpermitting climate simulations as computational capabilities advance. From a model evaluation point of view, convection-permitting simulations enable direct comparison of model simulated clouds with those observed using satellite and ground-based radar measurements from long-term observations or field campaigns. For example, radar and satellite observable variables like reflectivity, cloud size, and depth do not have meaningful counterparts in traditional global climate models (GCMs) but can be simulated in cloud-permitting models. In traditional GCMs one can only compare the aggregate effects of clouds on the resolved scale thermodynamics such as diabatic heating and moistening. Several studies that evaluate the organization of convection in such limited area convectionpermitting models point to issues in the representation of microphysical processes. For example, the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) field campaign was used to evaluate and identify biases in limited area convection-permitting model simulations by Zhu et al. [2012]. They found large deviation in dynamical fields (such as divergence) from observations and intermodel differences in microphysical fields, especially for ice particles. Earlier Wang et al. [2009] evaluated the performance of various bulk microphysical schemes in WRF against ground- and satellite-based retrievals and showed that much of the errors and uncertainties in model precipitation are associated with the differences in the treatment of cloud ice, snow, and graupel particles. A recent examination of the simulation of the number and size of tropical Mesoscale Convective Systems (MCSs) against satellite observations by Van Weverberg et al. [2013] showed that errors in the sizes of model-simulated MCSs are related to the number concentration and fall velocity of ice particles. The mean precipitation rate was overestimated in all the microphysics schemes they considered. In an evaluation of cloud-resolving model-simulated cloud and precipitation against observed radar reflectivity and infrared brightness temperatures during the active period of the Australian monsoon, Varble et al. [2011] showed that many of the cloud-resolving models run with periodic boundary conditions tend to underestimate the stratiform rain rate while overestimating convective rain and stratiform rain area. Furthermore, through sensitivity simulations they also showed that the simulated reflectivities are most sensitive to intercept and shape parameters in snow and graupel size distributions. The high reflectivity and excessive convective rain bias were also noted in an evaluation of the System for Atmospheric Modeling (SAM) against radar observations during Kwajalein Experiment by Blossey et al. [2007]. In that study, the authors argue that this bias is related to the fact that the model precipitates large hydrometeors too efficiently. Liquid water processes can also influence precipitation organization. Morrison et al. [2012] showed that parameterization of raindrop breakup has an important impact on the intensity of midlatitude squall lines. They found that more aggressive breakup process reduces the number of large droplets in favor of smaller droplets with slower sedimentation speeds that are more vulnerable to evaporation. The evaporative cooling and associated cold pool dynamics could force secondary boundary layer updrafts and moist air convergence at their edges that could generate new convection [Tompkins, 2001; Schlemmer and Hohenegger, 2014]. Hong et al. [2010] also note the importance of droplet number concentrations to the evolution of convective systems both over land and oceanic environment. They showed that in comparison to a previous version, a wider range of droplet and rain particle concentrations enhances evaporation over the nonprecipitating cloud regions and resulted in improved simulation of the statistics of precipitation and the evolution of convection for squall lines over the United States Great Plains, oceanic cyclogenesis, and summer monsoon rainfall over East Asia. Whether the impact of these processes hold true over oceanic environment, where boundary layer and surface properties are significantly different, warrants further investigation. Observations collected during the 2011 Atmospheric Radiation Measurement (ARM) MJO Investigation Experiment and Dynamics of the MJO (hereafter collectively referred to as AMIE/DYNAMO) field campaign [Yoneyama et al., 2013] over the equatorial Indian Ocean provide a unique opportunity to evaluate the performance of these bulk microphysics schemes in a tropical oceanic environment far from continental (monsoon) influences. This study aims to evaluate tropical organized convection simulated by a convection-permitting WRF model using four bulk microphysics schemes against radar and surface meteorology data collected during the

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AMIE/DYNAMO field campaign and the sensitivity of this bias to various hydrometeor size parameters related to the raindrop breakup processes.

2. Data and Model 2.1. The AMIE/DYNAMO Field Campaign Data During AMIE/DYNAMO, the National Center for Atmospheric Research (NCAR) S band (10 cm wavelength) Polarimetric radar (S-Pol) was deployed at Addu Atoll (0.6°S, 73.1°E) in the Maldives from 1 October 2011 through 15 January 2012. The S-Pol radar has a 0.91° beam width with a maximum range of 150 km. The resolution of the radar at 50, 100, and 150 km is approximately 0.8, 1.6, and 2.4 km, respectively. Comparison with collocated cloud radar measurements during AMIE/DYNAMO shows S-Pol has excellent sensitivity in detecting a wide spectrum of precipitating and nonprecipitating clouds and a minimum sensitivity of ~0 dBZ at its maximum range [Feng et al., 2014]. The scanning strategy included plan position indicator (PPI) scans for mapping the entire area of radar coverage (360° of azimuth) and a set of elevation angle scans referred to as RHI (range height indicator) scans over limited areas of the radar for increased vertical resolution. The PPI scans were performed at eight elevation angles ranging from 0.5° to 11.0° and recorded reflectivity data with a 1° azimuthal resolution. The RHI scans recorded data at 0.5° incremental elevation angles up to 40°. The reflectivity data are gridded to 2 km in the horizontal and 0.5 km in vertical Cartesian grids up to 20 km in the vertical using the NCAR Radx package [Mohr and Vaughan, 1979]. The method is based on an eight-point 3-D linear interpolation. For each grid point, the surrounding eight valid radar values are considered in the interpolation. While AMIE/DYNAMO was a much more comprehensive field campaign with many more instruments and a wide range of data collected (see Yoneyama et al. [2013] for details), only the reflectivity data from the RHI scans, rain rate estimates from the PPI scans of the S-Pol radar, and the R/V Revelle shipborne near-surface meteorological measurements are used in this study. The R/V Revelle was stationed at 0°, 80.5°E from 12 November to 25 November 2011. The S-Pol rain rate estimate used in this study is produced using the “hybrid algorithm” [Ryzhkov and Zrnić, 1995], which is a combination of a simple Z-R relationship and several more advanced polarimetric parameters based methods (for more details see the S-Pol rain rate computation documentation http://www.eol.ucar.edu/projects/dynamo/spol/parameters/ rain_rate/rain_rates.html). The released version of the S-Pol rainfall data product at the time of this study follows the Z-R relationship derived from the 2006 Mirai Indian Ocean cruise for the Study of the Madden-Julian oscillation (MJO)-convection Onset (MISMO) field campaign [Yoneyama et al. 2008]. Verification of the rainfall product using surface disdrometer data on the Addu Atoll is currently an active research effort. E. Thompson et al. (submitted manuscript, 2014) reported that the MISMO Z-R relationship is very similar to that derived from the in situ disdrometer measurements, and the potential bias in the current rainfall product is ~10%. Further validation and improvement of dual polarimetric-based radar rainfall product is currently underway. We used the released version of the S-Pol rainfall product available to us at the time of this study and acknowledged that future improvements and uncertainty reduction are possible. 2.2. Model and Simulation Design The model used in this study is the Advanced Research Weather Research and Forecasting Model V3.4.1. The eight simulations performed use four common two-moment bulk microphysics schemes. These are Thompson [2008], Milbrandt and Yau [2005], Morrison [2005], and the WRF Double-Moment 6 class (WDM6) [Lim and Hong, 2010]. All the simulations are run from 1 November to 30 November 2011. The lateral forcing and sea surface temperatures are provided from ERA-Interim reanalysis and are updated every 6 h. The Mallor-Yamada-Janjic [Janjic, 2001] planetary boundary layer scheme and Monin-Obukhov-Janjic [Janjic, 2001] surface scheme are used. The simulations use Dudhia [1989] shortwave and Rapid Radiation Transfer Model-Global [Iacono et al., 2000] long-wave radiation schemes. In the first set of four simulations discussed in the next section, all parameters in the various microphysics schemes are kept at their released default values (hereafter V1). The other set involves modifications to the parameterization of raindrop breakup process to investigate sensitivities. The simulations that involve modifications to model microphysics parameterizations will be referred to as Version 2 (V2). The details of the modifications and their effects on the simulated cloud populations are discussed in section 2.4 (d). The model domain is a 24° longitude × 12°

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Figure 1. The simulation domain with a snapshot of instantaneous rain rate. (a) The area of the S-Pol radar (located at Addu Atoll) is marked by the circle (150 km radius), and the location of the Revelle ship is marked by the diamond, (b) model simulated echo-top heights (see text for details) subsetted to match the S-Pol RHI scanning area, and (c) model simulated rain rate subsetted to match the S-Pol PPI scanning area. The gray shading in (Figures 1b and 1c) is masked out in the same way as the S-Pol data.

latitude region including the locations of the S-Pol radar and the R/V Revelle (Figure 1a) with 2 km horizontal grid spacing and 40 vertical levels. This version of the model has a built-in radar simulator that uses the specified hydrometeor size distributions assumed in each microphysics scheme to calculate radar reflectivity following Smith [1984]. Using the same radar simulator to calculate radar reflectivity from particle size distribution from each of the individual microphysics schemes ensures consistency in the comparison. Reflectivity from the radar simulator imbedded in the model is written out as an output variable. The simulator corresponds to a 10 cm wavelength radar; thus, it is equivalent to the S-Pol radar observations. In order to utilize the higher-vertical resolution, the region of the RHI scan (Figure 1b) is used in comparisons that involve echo-top heights. The circular region shown in Figure 1c is used for comparison of near-surface precipitation fields from the S-Pol PPI scans with the modelderived precipitation. 2.3. Partitioning of Convective and Stratiform Precipitation Precipitation is partitioned into convective and stratiform using the horizontal reflectivity texture-based algorithm developed by Steiner et al. [1995]. This method has been used in previous studies to facilitate direct comparison between radar observations and model simulations [e.g., Fridlind et al., 2012; Varble et al., 2011; Wu et al., 2013] and is particularly useful when fine-scale dynamical (e.g., in cloud vertical velocity) measurements are not available to aid the partitioning, as was the case for AMIE/DYNAMO field campaign. The Steiner et al. [1995] technique uses the 2-D reflectivity field at 2.5 km height and is applied to the S-Pol radar observed and model simulated reflectivity consistently. A radar/model grid point is classified

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as convective if its reflectivity value is above 38 dBZ or it exceeds its background intensity threshold, calculated by averaging the reflectivity values in an 11 km radius circle centered on the grid. The exceedance threshold decreases as a cosine function from 10 dB (at a mean background value of 0 dBZ) to 0 dB (at a mean background reflectivity of 38 dBZ) with increasing mean background reflectivity (see Figure 7 in Steiner et al. [1995]). For example, a grid point with a 2.5 km reflectivity value of 15 dBZ can be classified as convective if its associated background mean reflectivity is 0 dBZ and the exceedance value is 15 dB. Grid points within up to a 5 km radius (depending on background reflectivity value, see Figure 6 in Steiner et al. [1995]) of the convective pixel are also classified convective. The remaining echoes that exceed 5 dBZ but are not classified as convective are labeled as stratiform. The S-Pol radar estimated and model simulated rain rates (with associated reflectivity >5 dBZ) are partitioned into convective/stratiform components using this method. After partitioning convective/stratiform precipitation for each grid point, the connected convective pixels are grouped and labeled as a “convective cell.” Details of the method for deriving near-surface precipitation from radar observations and its uncertainty estimates are provided in Appendix A. 2.4. Raindrop Breakup/Self-Collection Process The sensitivity of modeled cloud, precipitation, and cold pool statistics to raindrop size distribution is assessed by analyzing the effects of a more aggressive raindrop breakup/less efficient self-collection process on their corresponding statistics. In Thompson and Morrison microphysics schemes, raindrop self-collection follows the parameterization in Beheng [1994], and the raindrop breakup is based on Verlinde and Cotton [1993]. In the WDM6 and Mibrandt and Yau microphysics schemes the raindrop self-collection and breakup processes follow Cohard and Pinty [2000]. Figure 2 shows the dependence of the raindrop self-collection/breakup efficiency (Ec) on droplet size in the control (V1) and the sensitivity (V2) runs. The formulations and the changes introduced for the sensitivity test simulations are listed in Table 1. Positive Ec values in Figure 2 can be interpreted as the efficiency of raindrop self-collection. Both control (V1) and sensitivity (V2) simulations with Thompson scheme have positive Ec when the raindrop diameter is smaller than 1.9 mm. Ec of the Thompson scheme is reduced in the sensitivity (V2) run by a factor of 8 compared to the control run, which means that the raindrop self-collection is less efficient, thus leading to larger numbers of raindrop in the sensitivity run. Ec in the WDM6 and Mibrandt and Yau schemes resides in a range between 0 and 1 and can be interpreted as a perturbation affecting the formulation of the raindrop self-collection term as described in Cohard and Pinty [2000]. The negative Ec in the Morrison scheme can be interpreted as the efficiency of breakup of raindrop. Larger negative Ec in the sensitivity test means more efficient breakup processes, thus leading to increased raindrop number concentrations. The implementation of the raindrop self-collection/breakup processes and the threshold parameters that control Ec vary from one microphysics scheme to another. The difference in Ec between V1 and V2 becomes larger when the raindrop diameter decreases in Thompson, Mibrandt and Yau, and WDM6 (Figures 2a and 2b). Meanwhile, the difference in Ec between V1 and V2 becomes smaller when the raindrop diameter increases in Morrison scheme. The Thompson scheme has the largest difference in Ec value for smaller raindrops between V1 and V2 runs. The impacts of the parameter changes on the droplet size distribution (DSD) are examined. The volumetric percentage contribution of each 0.01 mm droplet size bin to rain rate of 10 mm h
1 is shown in Figures 2a–2d (right). As the Ec parameter changes, the DSD is broadened with both large (>0.5 mm radius) and smaller (0.6 mm radius), while for the Morrison scheme the DSD is shifted toward smaller droplets with a large decrease in the number of large (>0.6 mm radius) droplets.

3. Comparisons of Model Results With AMIE/DYNAMO Measurements 3.1. Rain Rate and Areal Coverage The mean rain rate and mean rain areas averaged over the PPI scan domain and the entire simulation period are partitioned between convective and stratiform as described in Appendix A are shown in Figure 3. In general, all microphysics schemes underestimate the stratiform rain rate averaged over the whole PPI scan

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Figure 2. (a–d) The efficiency of self-collection and raindrop breakup (Ec) as a function of droplet radius in the microphysics schemes. V1 corresponds to the control (default) simulations and V2 corresponds to the parameter changes introduced to reduce droplet sizes in the sensitivity test simulations. Figures 2a–2d (right) show the volumetric droplet size distribution (%) at 10 mm/h rain rate.

domain. They reproduce the convective rain rate comparatively well except for WDM6 (Figure 3a). On the other hand, the model simulations produce stratiform areas that are comparable to what is observed by the radar, while convective area is overestimated in general. Again, WDM6 is an exception. This result suggests that for the three simulations (excluding WDM6) the rain rate (per grid point) from the convective cells is generally underestimated (because the comparable rain is coming out of a larger area) and the rain rate in the stratiform areas is also underestimated (because a smaller amount of rain is coming out of comparable area).

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Figure 3. Scatterplot of (a) the mean convective and stratiform rain rate (mm/h) and (b) the mean area fraction of the convective and stratiform rain. All are averaged over the same area in Figure 1c and over the month of November 2011. Open circles are for default microphysics version (V1), and filled circles are for the revised version (V2). The gray region indicates that the error bars for S-Pol in Figure 3a denote uncertainty of S-Pol estimated convective/stratiform rain rate (see Appendix A for details), and the shaded region is bounded by the fit lines of the uncertainties in convective and stratiform rain, respectively. The dashed line marks where the points would lie if they correctly reproduced the convective/stratiform rate (area) fraction.

Introducing a more aggressive breakup/less efficient self-collection process (solid circles) shifts the partitioning of rain rates in the Morrison, Thompson, and WDM6 schemes slightly toward what is observed but not for the Milbrandt and Yau scheme. It slightly improves the rain area fraction for only the Morrison and Thompson schemes. The rainfall statistics and cloud populations reported in section 3 are not sensitive to the location of the analysis domain (Figure 1). Sampling the simulated rainfall and cloud population at other longitudinal locations (but same latitude) with the same domain size from the model yields similar results (not shown), suggesting that the results are quite robust in representing the model performance even though the analysis domain is small in comparison to the simulation domain. This is expected given the large size of the convective clusters associated with the MJO, and the fact that our statistics are derived from a month long simulation. The grid point convective and stratiform rain rate statistics are examined by considering their probability distribution (Figure 4). The minimum rainfall rate in the S-Pol data is 0.10 mm/h. In comparison to observations, the schemes underestimate the frequency of convective rain rates larger than 5 mm h
1 and stratiform rain rates larger than 0.5 mm h
1, except for WDM6 which overestimates the frequency of stratiform rain rates higher than 5 mm/d
1. But stratiform rain area is severely underestimated by WDM6 schemes such that the overall stratiform rain amount is underestimated (Figure 3). Note that the rain rate distributions from the S-Pol at both 2.5 km and 0.5 km height are shown (gray shaded region between the two probability density functions (PDFs)), and the simulated high rain rate frequencies are underestimated regardless of which S-Pol PDF it is compared to. The changes in the breakup and self-collection parameters have little effect on the probability distributions. Even though the biases in stratiform area fraction and convective rain rate discussed above (Figure 3) have opposing effects on the bias in domain mean rain rate, they do not completely compensate for each other and the latter is generally underestimated, particularly in WDM6. Figure 5 shows these comparisons as time series of surface precipitation averaged over the circular PPI scan region (Figure 1c). In general, the models capture precipitation in the suppressed MJO period during the first half of November as well as the precipitation peaks associated with arrival of the major precipitation at Addu Atoll during the second half of the month. The mean precipitation for the month for each pair of simulations is shown in the legend. Overall, the more aggressive breakup/less efficient self-collection has little effect on the time series or monthly mean value of model precipitation. 3.2. Reflectivity, Size, and Echo-Top Height of Convective Cells In order to take a closer look at the nature of the bias in convective cloud populations, individual convective cells are identified as described in section 2c. For each convective cell, the size (area) averaged echo-top

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Figure 4. Probability density function (PDF) of pixel-level (left column) convective and (right column) stratiform rain rates from the two sets of simulations and the S-Pol radar. The S-Pol rain rate PDFs at 0.5 km and 2.5 km heights are both included and shown as shaded gray region (0.5 km rain rates are higher). Mean values are provided in mm/h in the legend.

height and reflectivity at 2.5 km are calculated, such that only a pair of echo-top height and reflectivity values represents that convective cell. Echo-top height is computed from bottom up using a 10 dBZ threshold to represent the height of the precipitating clouds. Individual pixel reflectivity values in the convective cells are first converted to linear units (mm6 m
3), averaged, and then converted back to a logarithmic unit (dBZ). Sensitivity tests using different reflectivity thresholds (i.e. using 0 dBZ and 20 dBZ) shift the echo-top heights up (0 dBZ) or down (20 dBZ) by 1–2 km for both S-Pol observations and model simulations. Using different thresholds does not change the conclusions in this study. The 10 dBZ echo-top heights were chosen because the radar simulator used in this study only includes precipitating hydrometeors (rain, graupel, and snow) in calculating the reflectivity, while the S-Pol radar is capable of detecting some small ice crystals near cloud top [Feng et al., 2014]. Therefore, using a 10 dBZ threshold is more appropriate for the comparison in the upper portion of the convective clouds where larger ice particles are present in both S-Pol and model simulation. After the averaged 10 dBZ echo-top height and reflectivity at 2.5 km are computed for each convective cell, a two-dimensional probability distribution of convective cell mean reflectivity is constructed. This is done by partitioning the 1 km to 18 km echo-top heights into 18 equally spaced bins. Similarly, the cell areas are

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Figure 5. Time series of mean rainfall within the S-Pol PPI scan area (black line) from S-Pol (black lines), default microphysics (V1, green), and revised microphysics (V2, red). The gray shading to the black line denotes uncertainty for S-Pol estimated rainfall. Numbers in the legends are averaged values over the entire month.

partitioned into 20 bins that range from 12 km2 (3 connected pixels) to 10,000 km2 (2500 connected pixels). Because of the nature of the distribution of the cell sizes, logarithmic scales are used for the areal dimension of the bins and therefore they are not equal in size. Once the area-echo-top height bins are constructed, the cells are assigned to their corresponding bins and the mean reflectivity is calculated for each bin. This is done for the S-Pol radar observed convective cells and for those from the model simulations. Figure 6 displays the distribution of reflectivity with respect to cell size and echo-top height for the control runs (V1). The reflectivity is shaded and number of samples within each bin is shown in contours. In both the models and the S-Pol observations, large and deep cells are associated with higher reflectivity at 2.5 km. In general, the models overestimate reflectivity from deep, large convective cells. However, the nature of this bias varies from scheme to scheme. The Milbrandt and Yau and WDM6 schemes produce relatively high reflectivities from convective cells with echo-top height lower than 6 km, which is almost never observed by the radar. In comparison, the Thompson and to a lesser extent Morrison schemes capture the observed relationships between reflectivity and echo-top height better. In Figure 7 we show the same distributions for the sensitivity runs (V2). The more aggressive breakup/less efficient self-collection reduces the high reflectivity from large deep convective cells to a varying degree from strongly in the case of the Morrison

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Figure 6. Averaged convective cell reflectivity at 2.5 km height as a function of cell area and mean cell echo-top height. Model simulations are with default setting of microphysics schemes (V1). The contours represent the number of cells in the bin.

scheme to very little in the case of Milbrandt and Yau and WDM6. This is consistent with the fact that the changes in the self-collection/breakup parameters in the Morrison scheme shift the DSD toward smaller droplets (lower reflectivity) while those in others merely broadened it. In a similar framework, the relationship between echo-top height and areal size of the convective cells is also examined. This provides an insight into the nature of the organization and entrainment processes. All else

Figure 7. Same as Figure 5 except for revised microphysics schemes (V2).

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Figure 8. Joint PDF of convective cell radius and mean cell echo-top height. P represents the slope of the fit line, i.e., P het = (Req) . The green and red lines are the fit lines for the default (V1) and modified (V2) simulations.

being equal, moist convection will grow deeper if the entrainment of dry environmental air is weaker. One of the factors that affect this entrainment is the size of the clouds. As clouds grow larger, more of their interior is shielded from mixing with dry environmental air; and therefore, they are more likely to grow deeper. Figure 8 qffiffiffiffiffiffi shows this quantitatively. The number of cells with a specific size (in equivalent cloud radius Req ¼ Aπcell , where Acell is the convective cell area) and echo-top height is divided by the total number of cells with that size. Since both the echo-top height and equivalent convective cell radius are on logarithmic scale, the relationship between them can be approximated by power law het = (Req)P, where het is the mean echo-top height of the cells in the bin and P is the approximate slope of the straight line that best fits the relationship. P is found to be 0.28 for the S-Pol radar observed convective cells. For the control runs (represented by the green lines in Figure 8), P is 0.40, 0.41, 0.42, and 0.28 for the Thompson, Milbrandt and Yau, Morrison, and WDM6 schemes, respectively. For the sensitivity runs (represented by the red lines in Figure 8) these values are 0.33, 0.42, 0.40, and 0.25, respectively. The difference in P between the observations and the model simulations reflects the fact that the model simulated small shallow convection during the suppressed phase of the MJO tends to be slightly shallower than those observed (except those from WDM6 where both large and small cells are relatively shallower). The changes bring the model simulated slopes closer to the observations except for Milbrandt and Yau. Despite their differences, all schemes agree with the radar observation on the power law like relationship between convective cell echo-top height and radius, indicating that the model turbulence parameterization captures some of the entrainment and detrainment processes that affect cloud depth. The temporal evolution of the frequency of the convective cells with a given echo-top height is shown in Figure 9. The stepwise deepening of convection is apparent in the observation and model simulations. In the S-Pol observation, during the first week of November the most frequent echo-top height is between 4 km and 6 km. In the second week, it is between 5 and 6 km and during the third and fourth weeks when the major precipitation events are detected the mode echo-top height reaches as high as 8 km. This behavior is captured by the model simulations to a varying degree, with the Thompson and Morrison scheme most closely tracking the observations. The effect of the changes in the self-collection/breakup parameters (red lines) is marginal in that respect.

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Figure 9. Time series of 10 dBZ convective echo-top height frequency from (a) S-Pol observation and model simulations with (b) Thompson, (c) Milbrant-Yau, (d) Morrison, (e) WDM6 microphysics. Frequencies are calculated at 12-hourly intervals and normalized to 1 at each time. The black line in Figure 9a is the mode distribution of echo-top heights and repeated in Figures 9b–9e, and the green (red) lines in Figures 9b–9e are the V1 (V2) mode distributions from the mode simulations. White vertical bars are periods when no convective cells are detected in the domain.

3.3. Sensitivity of Cold Pool Statistics to Raindrop Breakup/Self-Collection Parameters As noted in previous studies [Morrison et al., 2012; Van Weverberg et al., 2011], the droplet size distribution of hydrometeors influences their evaporation rate as they fall through the subcloud layer to the surface and hence affects generation of cold pools. Using the near-surface temperature observations by R/V Revelle, we assessed the degree to which the control versions of the microphysics schemes reproduce the observed cold pool frequency. We also tested how the more aggressive breakup/less efficient self-collection processes introduced in the sensitivity runs affect the modeled cold pool frequency. The definition of cold pools and the methodology used to identify them in the R/V Revelle surface meteorology measurements and model simulations are described below. Likely cold pool events are detected in the 10 m air temperature time series from the model output and the observations collected on the R/V Revelle. Data from the ship are subsetted from their native 1 min temporal resolution to match the 10 min output of the models. Observational data from November 2011 are further restricted to an area ±5° latitude from the equator and from 75°–85°E to roughly match the spatial domain of the model. From the model output, 40 data points with a 1° spatial separation are selected and a time series of the simulated near-surface air temperature is output for each of the points. We use a metric of cold pools per day at a given point to reduce the biases from differences in sample size between the model output and the observations. The temperature time series from the models and observations are filtered using a simple Haar wavelet filter with a window width of ±2 data points (20 min). Cold pools are identified as temperature decreases larger than
0.5°C in the filtered temperature time series. The mean temperature change across the cold pool boundary is between
1°C and
2°C in both the model output and near-surface observations. Cold pools in both data sets are characterized by an increase in wind speed, mixing ratio, and latent heat flux (not shown). HAGOS ET AL.

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Figure 10. Frequency of cold pools detected at the surface from Revelle ship observations and similar locations selected from the model simulations.

The results are not sensitive to the choice of temperature difference threshold. Using
0.25°C and
1°C to define cold pools yields similar conclusions. Figure 10 shows comparison of the cold pool frequencies from the control and sensitivity experiments. In the control simulations, the frequency of cold pools in the model simulation is less than about half of that observed. Introducing more aggressive raindrop breakup/less efficient self-collection processes in the sensitivity runs consistently increases these frequencies as one expects from enhanced evaporation of smaller droplets.

Figure 11. Probability density function (PDF) of surface latent heat flux from the two sets of simulations and the RV Revelle 2 2 observations. Mean values are provided in W/m in the legend. The latent heat flux bin size is 10 W/m .

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The model is able to simulate near-surface temperature, wind speed (not shown), and the mean and probability distributions of latent heat fluxes reasonably well in comparison to those observed by the R/V Revelle, with little sensitivity to microphysics scheme (Figure 11). This suggests that the oceanic boundary layer is fairly well captured by the model. Results show that the sensitivity of surface latent heat flux to microphysics schemes is quite small. The changes in the self-collection/breakup processes increase the frequency of cold pools and thereby increase the mean surface fluxes, but these changes (only around 1%) are small (Figure 11). For the same reason, the associated changes of fluxes are unlikely to affect the overall convective organization and therefore do not have significant impact on the overall precipitation amount or convective/stratiform fraction. Such examination of the sensitivity of surface fluxes along with rainfall properties to variations in microphysical parameters in a model could provide useful constraints on these parameters, because true improvement in parameterization has to lead not only to improved rainfall statistics but also surface fluxes and the overall energy and water budget of the model.

4. Discussion This study examines simulations of organized convection observed by the NCAR S-Pol scanning radar during the AMIE/DYNAMO field campaign in November 2011 by a WRF regional cloud-permitting model with four state-of-the-art two-moment bulk microphysics schemes. Three major rain events associated with a Madden-Julian Oscillation episode were observed by S-Pol in November 2011. The horizontal and vertical cross-section scans by the radar provided a comprehensive view of oceanic convection associated with the MJO. The statistics of the observed depth and size of precipitating convective cloud populations are compared to those simulated by the model. Near near-surface temperature measurements from the R/V Revelle were used to examine the observed and modeled cold pool occurrence frequency. A second set of simulations is performed with a more aggressive raindrop breakup/less efficient self-collection efficiency in the microphysics parameterization implemented in order to examine the sensitivity of modeled cloud, precipitation, and cold pool statistics to raindrop-sized distributions. The model simulations are run at 2 km grid-spacing with a domain that includes the radar site and the location of the ship with lateral boundary condition obtained from ERA-Interim reanalysis (Figure 1). In general, in the control runs, most of the microphysics schemes underestimate stratiform rain rates by more than 50% and overestimate convective rain areas by more than 50% in comparison to that observed by the S-Pol radar (Figure 3). Even though these two biases compensate for each other, the domain mean precipitation values from most of the microphysics schemes are still underestimated to a varying degree in comparison to radar observations. However, in agreement with the findings of Blossey et al. [2007], the reflectivity associated with deep and large convective cells is consistently overestimated by the model simulations. In the sensitivity runs, where the raindrop size distribution is modified by a more aggressive breakup/less efficient self-collection process, the response varies from scheme to scheme. A slight reduction in the reflectivity bias is observed in the Morrison and Thompson scheme but little changes in the Milbrandt and Yau and WDM6 schemes. In contrast to the findings of Morrison et al. [2012], the domain mean surface precipitation is generally unaffected by the more aggressive raindrop breakup, likely because the overall impact of changes in cold pool frequency on the surface fluxes is relatively small. Another possible explanation is the decline in precipitation by increased evaporation is compensated for by increased precipitation from cold pool induced secondary updrafts. The high-reflectivity and low-rain rate biases suggest that the model-simulated raindrop size distributions may be inconsistent with observations. Evaluation of the model raindrop size distributions against surface disdrometer measurements may provide additional insight and constrain to the microphysics parameterizations and will be explored in future studies. The model simulations and the radar observation are found to be fairly consistent in their depiction of the relationship between echo-top height and convective cell size. A power law relationship is obtained from echotop height and equivalent convective cell radius. The power for this relationship for the S-Pol radar observed convective clouds is found to be about 0.28, while those from the model vary from 0.28 to 0.42 (Figure 8). The model simulations capture the temporal evolution of convective cell echo-top height with the passage of the November 2011 MJO fairly well (Figure 9). The frequency of both observed and modeled deep convective cells rapidly increases with the passage of the two major precipitation events during the second half of the month. Comparison of the frequency of cold pools derived from model and ship-based measurement of

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Figure A1. Scatterplots of domain averaged S-Pol observed RHI rain rate (x axis) versus converted RHI rain rate (y axis) for (a) convective rain and (b) stratiform rain. The blue error bars denote standard deviation for each bin with equal number of samples, and the shaded region shows the upper/lower bounds of error associated with the conversion function. Linear fitting functions of the upper/lower bounds of error are given in the legend.

near-surface temperature reveals that the control simulations produce about half of the frequency of observed cold pools. The enhanced raindrop break up consistently increases the cold pool frequency and alleviates some of this bias in the sensitivity runs to a certain degree (Figure 10), although their impact to surface fluxes and overall precipitation amount is small due to the relatively low frequency of occurrence. The study highlights the utility of using integrated radar and near-surface measurements to evaluate the representation of cloud populations, precipitation, and cold pools by microphysics schemes and sensitivities to the parameters therein. While the goals of completely eliminating some biases such as underestimated stratiform rain rates and overestimated convective rain areas and reflectivity remain elusive, domain mean precipitation and size-depth relationships of convective cloud populations are fairly well represented by the schemes evaluated in this study. The recent trend toward convection-permitting regional and global models for research and forecast applications will likely continue to be a fruitful endeavor as long as the biases discussed above, and possibly many others, are kept in mind when interpreting results of such modeling strategy.

Appendix A: Deriving Near-Surface Rain Rate From Radar Observations Area rainfall estimation from ground radar observations is typically produced at certain heights above the surface (e.g., 2.5 km) for several reasons: (1) the height of the lowest elevation radar beam increases with distance from the radar due to the curvature of the Earth, thus limiting the areal coverage near the surface; (2) beam blockage by ground objects such as trees close to the radar significantly affects the lowest radar sweep; and (3) ground/sea clutter greatly affects reliability of rainfall estimates close to the surface. Therefore, public release of the S-Pol radar rain rate estimates are provided at 2.5 km height above mean sea level to maximize the areal rainfall coverage and minimize the impact from ground/sea clutter. However, WRF model output rainfall is given at the surface. This prevents direct comparison with S-Pol radar observations. To mitigate this issue, a function to convert the S-Pol estimated rain rate from 2.5 km height to 0.5 km height is derived for this study. Due to potentially large variability in individual rainfall profiles, this conversion is only performed for domain averaged hourly convective and stratiform rain rates. The gridded S-Pol data product (provided by S. Brodzik (University of Washington, personal communication, 2014)) contains rain rate at each 0.5 km height level. Rain rates from the PPI scan at 0.5 km and 2.5 km heights between 20 and 80 km east of the radar, where beam blockage is minimal, are averaged for convective and stratiform grid points for each 15 min PPI scan and then further averaged to 1 h time increments. Linear fitting functions between the 0.5 km and 2.5 km averaged rain rates are then obtained: RCð0:5 kmÞ ¼
0:008215 þ 1:20936  RCð2:5 kmÞ RSð0:5 kmÞ ¼ 0:001703 þ 1:10973  RSð2:5 kmÞ where RC and RS denote PPI convective and stratiform rain rates, respectively. HAGOS ET AL.

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It is evident from the two equations above that both convective and stratiform rain rates at 0.5 km are higher than that at 2.5 km (~15% differences). To estimate the uncertainty associated with these conversion functions, we applied them to the rainfall estimates from the independent RHI data set. Since the RHI scanned vertically from elevation angles of 0.5° to 40°, high-vertical resolution rainfall data are available at both 2.5 km and 0.5 km for verification. The 2.5 km RHI hourly mean convective and stratiform rain rates are converted to 0.5 km using the above two functions and then compared to the actual RHI observed mean rain rates at 0.5 km to estimate the error (Figure A1). The data are divided into 20 bins with an equal number of samples, and the standard deviations of each bin are calculated. The upper and lower bounds of the errors associated with the conversion functions are given in the legend for both convective (EC(high)/EC(low)) and stratiform (ES(high)/ES(low)) rain. Errors generally increase with rain rate. This uncertainty is provided as shaded region in Figure 4, and the average uncertainty is given in the legend therein. Acknowledgments The authors thank Yun Qian for his comments and suggestions. The data for this paper are available at NCAR’s Earth Observing Laboratory’s DYNAMO Data Catalogue https://www.eol.ucar. edu/field_projects/dynamo. The data set names are, R/V Roger Revelle Flux, Near-Surface Meteorology, and Navigation Data and S-PolKa Radar, fully corrected, merged, final moments data in cfRadial format. The S-Polka data were regridded by Stacy Brodzik ([email protected]) at the University of Washington. This research is based on work supported by the Office of Biological and Environmental Research of the U.S. Department of Energy (DOE) as part of the Regional and Global Climate Modeling Program and Atmospheric System Research Program. Computing resources for the simulations are provided by the National Energy Research Scientific Computing Center (NERSC) and Oak Ridge Leadership Computing Facility (OLCF). The Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute under contract DE-AC06-76RLO 1830.

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