Microwave Rainfall Estimation over Coasts - AMS Journals - American ...

VOLUME 22

JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY

MAY 2005

Microwave Rainfall Estimation over Coasts JEFFREY R. MCCOLLUM

AND

RALPH R. FERRARO

Cooperative Institute for Climate Studies, University of Maryland, College Park, College Park, Maryland (Manuscript received 10 June 2004, in final form 8 October 2004) ABSTRACT The microwave coastal rain identification procedure that has been used by NASA for over 10 yr, and also more recently by NOAA, for different instruments beginning with the Special Sensor Microwave Imager (SSM/I), is updated for use with Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Advanced Microwave Scanning Radiometer (AMSR)-[Earth Observing System (EOS)] E microwave data. Since the development of the SSM/I algorithm, a wealth of both space-based and ground-based radar-rainfall estimates have become available, and here some of these data are used with collocated TMI and AMSR-E data to improve the estimation of coastal rain areas from microwave data. Two major improvements are made. The first involves finding the conditions where positive rain rates should be estimated rather than leaving the areas without estimates as in the previous algorithm. The second is a modification to the final step of the rain identification method; previously, a straight brightness temperature cutoff was used, but this is modified to a polarization-corrected temperature criterion. These modifications are made for the TRMM version 6 product release and the third (1 September) release of AMSR-E products to the public, both in 2004. The modifications are slightly different for each of these two sensors.

1. Introduction Damage and deaths resulting from the direct and indirect effects of rainfall associated with landfalling tropical systems in the United States exceed those that are caused by both wind and wave damage (Rappaport 2000). The continental United States alone has over 18 000 km of coasts for which the population density is much higher than inland, further increasing the risks that are associated with coastal storms. However, relatively little work regarding microwave satellite rainfall estimation over coasts has been done, because research has generally been divided into two categories—over ocean and over land. This division is logical because different physical principles are used over each surface; the ocean algorithms utilize the emission by liquid precipitation over the radiometrically cold ocean surface (Wilheit et al. 1991; Petty 1994; Kummerow et al. 2001; Bauer et al. 2001a) at lower microwave frequencies, while the land algorithms use the scattering by cloud ice at higher microwave frequencies (Ferraro 1997; Conner and Petty 1998; Grecu and Anagnostou 2001, Bauer et al. 2001b; McCollum and Ferraro 2003). Over coasts, the microwave footprint is a mixture of Corresponding author address: Jeffrey McCollum, CICS/ ESSIC/NOAA, University of Maryland, College Park, College Park, MD 20742. E-mail: [email protected]

© 2005 American Meteorological Society

JTECH1732

the radiometrically cold ocean and radiometrically warm land surface. Ferraro et al. (1998) used the scattering index of Grody (1991) and treated the coast similarly as in the land algorithm by using the cloud ice scattering signature to identify rain. Adler et al. (1994) were among the first to develop a global rainfall algorithm using microwave data [from the Special Sensor Microwave Imager (SSM/I)], and they developed a complicated decision-tree method for coasts to isolate possible rain from the similar brightness temperature (TB) signatures resulting from the different relative fractions of land and ocean in the footprint, without using the scattering index of Grody (1991). The method is described in more detail in Huffman and Adler (1993, hereafter HA93). The HA93 procedure was implemented in the Goddard Profiling Algorithm (GPROF; Kummerow et al. 1996) and has remained for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Advanced Microwave Scanning Radiometer (AMSR)-[Earth Observing System (EOS)] E versions thereof (Wilheit et al. 2003). Because the land and ocean components have improved considerably over several years as a result of many ongoing research efforts, the coastal component has remained virtually the same, even as the algorithm is applied to different microwave sensors than the SSM/I for which HA93 developed their method. Not surprisingly, the performance of the global algorithms has become worse over the coast than over land and ocean. This deficiency over the coast, particularly with

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TABLE 1. TMI instrument parameters. Center frequency (GHz)

10.65

19.35

21.3

37.0

85.5

Instantaneous field of view (IFOV) (km ⫻ km)

63 ⫻ 37

30 ⫻ 18

23 ⫻ 18

16 ⫻ 9

7⫻5

regards to the new versions released in 2004—the version 6 (V6) TMI algorithm and the preliminary (version 1) AMSR-E algorithm (McCollum and Ferraro 2003)— has motivated this work. In this study the abundance of validation radar data that has become available, particularly from the TRMM precipitation radar (PR), is used to improve the HA93 method for distinguishing rain over the coast using microwave data. While many studies have focused on the retrieval of rain rates, the important first step of rain identification is the focus of this study.

2. Background a. Algorithm framework GPROF has become the established algorithm framework for production of the microwave rainfall products from the National Aeronautics and Space Administration (NASA) EOS satellite programs that include rainfall, specifically, TRMM, which was launched in December 1997, and Aqua (the satellite of AMSRE), which was launched in May 2002. The use of GPROF is included in the initial plans for the proposed Global Precipitation Measurement (GPM) mission, which would merge microwave rainfall estimates from many different instruments into one global product. The GPROF framework is well suited for this effort because of its flexibility, as described below. GPROF calculates instantaneous rainfall rate estimates for each high-resolution (i.e., 85/89 GHz) footprint from the weighted average of rainfall rates from different vertical hydrometeor profiles that are created from numerical cloud models, primarily the Goddard Cumulus Ensemble model of Tao and Simpson (1993). Radiative transfer calculations for the frequencies and resolutions of any microwave instrument are done to produce a library of vertical profiles with the associated brightness temperatures. The profiles that are used for estimation are chosen and assigned weights based on the proximity of the observed microwave radiances to those of the library of profiles (Kummerow et al. 1996). Then, in addition to a surface rainfall estimate, the estimated profiles can be used to estimate latent heating profiles for model assimilation (Olson et al. 1999). The physical basis of GPROF is most useful over oceans, where the low and predictable oceanic emissivity gives contrast to the signal from liquid hydrometeors over the range of microwave frequencies. The high and variable emissivity of the land surface makes the information from the lower-frequency channels ambiguous (the same brightness temperatures result from both

rain and no-rain situations), so the ice scattering at higher frequencies is currently the most useful way to estimate rainfall over the land/coast. Cloud ice, which is associated with the surface rainfall (particularly in convective clouds), causes lower brightness temperatures at higher (in this study, 85/89 GHz) microwave frequencies. Empirical algorithms are based upon the relationship between surface rainfall and 85-/89-GHz TBs developed from radar and/or rain gauge surface rainfall estimates. GPROF can be used to produce results that are similar to an empirical algorithm, which is done in the land and coast components. The algorithms that are described here do not depend on whether the GPROF framework is used to produce the estimates, so the GPROF will not be described in detail here. The use of GPROF in this version is a starting point for future versions that will attempt to use physical principles over the land and coast as in the ocean algorithm to improve the rainfall estimates and produce better vertical hydrometeor distributions so that the latent heating estimates will improve. The first step of the algorithm is determination of the surface type of the high-frequency microwave footprint for which the rainfall estimate is made. For the TMI, the approximate footprint size of each frequency is given in Table 1. The frequencies and footprint sizes for AMSR-E are similar (Table 2) so they are used interchangeably between the TMI and AMSR-E rain identification algorithms; that is, the 21.3-GHz TMI (hereafter denoted as TB21V) and 23.8-GHz AMSR-E (hereafter TB24V or T24V; note that T and TB are used interchangeably) frequencies are used similarly. The differences in frequency are taken into account in the radiative transfer simulations that are used to construct the GPROF profile databases. There is an additional 6.9-GHz channel on AMSR-E, but it is not used in the algorithm. A static land–water–coast mask is used to determine the surface type of each satellite footprint; the V6 mask has a higher resolution than previous versions, including version 5 (V5). The mask must be sufficiently wide to account for the largest footprints that are more likely to have a mixture of land and water within the footprint, because the same lowresolution TBs are applied to the high-resolution TBs falling within the larger footprints. An image of the static 1/6° resolution global land– water–coast mask used in the V6 GPROF algorithm described here is illustrated in Fig. 1. Overall, 11% of the total number of global 1/6° grid boxes is classified as coast with this mask. Looking at an expanded view

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MCCOLLUM AND FERRARO TABLE 2. AMSR-E instrument parameters.

Center frequency (GHz)

6.925

10.65

18.7

23.8

36.5

89.0

IFOV (km ⫻ km)

75 ⫻ 43

51 ⫻ 29

27 ⫻ 16

32 ⫻ 18

14 ⫻ 8

7⫻4

(lower panel), a high fraction of this equatorial region is classified as coast because of the number of islands in this region. The thickness of the coast mask appears to be approximately 1° latitude ⫻ 1° longitude, which corresponds to ⬃100 km, or ⬃50 km on either side of the coastline. This is the approximate size of the largest (10 GHz) footprint, so a higher percentage of the larger footprints, for example, 10 GHz, will contain both water and land.

b. Rain identification over the coast Rain identification over either land or water is relatively straightforward. Aside from checking for other surfaces such as snow or desert that also depress the high-frequency (i.e., 85/89 GHz) microwave channels, rain is identified over land based on the high-frequency brightness temperature; for TMI and AMSR-E, 85-/89GHz TBs below 270 K are identified as rain in the current algorithms. The oceanic component of GPROF relies more on estimating the liquid water path, which is

FIG. 1. (top) Global GPROF land–water–coast mask and (bottom) an expanded view of the equatorial area indicated by the box on the upper panel. The black coloring designates the coast.

fairly accurate because of the contrast between the atmospheric liquid and low-emissivity ocean surface. If the estimated liquid water path is above a threshold that depends on the estimated freezing level height, the rainfall rate is retrieved by GPROF profile matching. Coastline is more difficult because for either land or water, adding the opposite surface into the footprint has the same effect as rain. Over land, adding surface water to the footprint will reduce the TBs, as does scattering caused by rain, and adding land to a water footprint will increase TBs, similarly to rain over water, resulting in emission. This is illustrated in Fig. 2 using the GPROF surface-type classification map. The less thick lines are for TMI water footprints, and these TBs increase with increasing rain rate, except for TB85V, which decreases above 1 mm h⫺1 due to scattering. The thicker lines are for footprints that are classified as coast, but starting with those footprints bordering the water classification and, thus, not containing much land. As the distance from the footprints classified as water increases, the fraction of land within the footprints and the TBs at all frequencies increase. One method to account for the complexity of the land–water mixture is to develop an algorithm to use the effective antenna pattern function and scan geom-

FIG. 2. TMI TB spectrum over water for increasing rain rates, and over nonraining coast with increasing land fractions for coastal data of South America, Africa, and Australia, May– Aug 2003.

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etry of the microwave instrument, along with a very high resolution land–water mask to estimate the fraction of land versus water in each footprint (Bennartz 1999). This fraction is used to weight the relative contribution of the two surfaces to the background signal, so that the atmospheric contribution from rain can be retrieved assuming a constant land TB. Bennartz (1999) did this to retrieve columnar water vapor for the nocloud conditions of a 2-month dataset for the Baltic Sea region of Western Europe, which contains a great deal of coast, using SSM/I data. He found that the satellite navigation uncertainty was the dominant source of error in his method. However, in the raining situations in which we are interested, we cannot use the method of Bennartz (1999) because we cannot assume the land TB to be constant; it will depend on the highly variable land surface emissivity. We still have yet to incorporate the land surface emissivity into the land component of the GPROF microwave rainfall algorithms; in the GPROF V6 land algorithm a straight cutoff for a high-frequency channel can be used for rain identification because lowered TBs are usually from scattering by cloud ice that is associated with rain. But, over coasts, water within the footprint can also reduce the high-frequency TBs (Fig. 2), so this method cannot be used either. Until the land surface emissivity is incorporated into the land portion of the algorithm (a planned modification for future versions), it probably should not be used for the coastal component. As a result, several criteria are examined to classify the footprint as no rain, possibly rain, or ambiguous; this is often called the “screening” procedure. This is what needs to be updated from the HA93 method and is addressed in this study. The HA93 decision-tree screening method is shown in Fig. 3. These criteria were determined from Grody (1991), Adler et al. (1993), observations with surface radar, and the calculation of long-term global means that showed obvious errors, such as massive amounts of estimated precipitation during the springtime thaw over Eurasia. The “clear coast check” from Adler et al. (1993) that is used in Fig. 3 identifies clear-sky coast cases as

␴共T85H兲 ⬎ 10 K ␳共T37H, T85H兲 ⬎ 0.5, and slope ⬍ 1.2,

共1兲

where slope ⫽ ␳(T37H, T85H) ⫻ ␴(T85H)/␴(T37H), and ␴ (standard deviation) and ␳ (cross correlation) are computed on a 5 ⫻ 5 footprint array centered on the footprint of interest. This test identifies cases in which low humidity allows the (similar) surface emission signals from T37H and T85/89H to dominate the microwave signal. HA93 added an ambiguous classification for TB combinations resulting in both false and real rain cases. A rainfall rate is retrieved for the ambiguous footprints

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with the intent that another scheme should be applied to estimate whether the retrieval is useful or an artifact. If this cannot be determined, no estimate is made for these footprints, leaving “holes” in the rainfall map, as in the example image shown in Fig. 4. The radar image is from the Eureka, California, coastal radar that is selected as an AMSR-E validation site. The Eureka validation project provides convenient access to coastal rainfall data to address this difficult problem of coastal rainfall estimation using passive microwave data, such as from AMSR-E. Rainfall estimates such as those in Fig. 4 were found to occur frequently using the HA93 method. For the coasts of South America, Africa, and Australia from May to August 2003, the TRMM PR detects rain for 5.2% of the coastal footprints, while the TMI detects rain for only 1.5% of the footprints using the HA93 method. This low probability of detection of 0.29, in addition to the frequent “holes” in the rainfall fields, prompted the modifications to the algorithm that are described in the following sections.

3. Development of new classification procedure using TRMM data The amount of data that are available for development and validation of microwave precipitation algorithms has increased significantly since HA93 developed their procedure. In particular, the launch of the TRMM satellite in 1997 has provided continuous collocated TMI brightness temperatures and TRMM PR rainfall estimates for the inner third of the TMI swath covered by the TRMM PR, with the TRMM orbit ranging ⬃38°N–38°S. While the TRMM PR rainfall estimates have their own uncertainties, they should be more accurate than the microwave-only estimates over the land or coast.

a. Reclassification of ambiguous footprints from TMI The first straightforward application of the TRMM PR data is to investigate the ambiguous classifications of the HA93 procedure. TRMM data from May to August 2003 for the coasts of South America, Africa, and South America provide hundreds of TMI coastal footprints that are classified as ambiguous with corresponding TRMM PR rain estimates, so that Table 3 can be created by calculating the percentage of the ambiguous TMI footprints where the TRMM PR estimates rain. Table 3 provides useful information for dealing with the ambiguous footprints. Approximately 80% of the classifications are from 63 to 65, so from these results it would be beneficial to classify these footprints as rain. For class 65, which comprises nearly 40% of the ambiguous classifications, the 82.8% rain percentage implies a false alarm ratio of 0.172 if all of these footprints were assigned positive rain rates. While this number is high, leaving these footprints as ambiguous would re-

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MCCOLLUM AND FERRARO

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FIG. 3. (a) Decision tree to identify possible coastal rain, from HA93 for SSM/I and applied to TMI (T21V for TMI instead of T22V for SSM/I). The initial modified criterion from this study is shown in italics. (b) The additional criteria developed in this study applied to the ambiguous classes 63, 64, and 65 emerging from (a) for TMI and AMSR-E data (T24V and T89H for AMSR-E).

sult in significant rain (82.8% of the cases based on this study) not being correctly classified as rain. Considering that overall the TMI has a low probability of detection over the coast (0.29 as stated previously), this opportunity to increase the estimated rainfall should be taken. For classes 63 and 64, the false alarms occur less frequently, suggesting that these footprints should also be assigned as positive rain. So for algorithm development, the ambiguous classes

63–65 will result in nonzero rain retrievals, which are subject to further validation later in this study. For class 66, only 11.8% of the footprints have positive PR rain. One idea may be to designate these footprints as nonraining, but considering that these footprints have a much higher probability of rain than the average footprint (over twice the PR rain percentage of 5.2%), it seems reasonable to leave these footprints as ambiguous.

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TABLE 3. TRMM PR rain percentages for different HA93 ambiguous classifications. Ambiguous screen classification

No. of footprints

Percentage of positive TRMM PR rain

63 64 65 66

694 436 1937 577

99.2 92.2 82.8 11.8

b. Previous rain/no-rain cutoff thresholds The “possible rain” identification procedure of Fig. 3 identifies footprints that are not in special situations, such as clear ocean, clear coast, or ice; after this, the possible rain footprints still must satisfy the basic requirement for rain over land—that the higherfrequency (i.e., 85/89 GHz) TBs are reduced because of scattering by cloud ice. The HA93 method uses a T85H threshold of 257 K; footprints below this value that satisfy the decision tree of Fig. 3 are assigned a positive rain classification. The 257-K T85H/T89H criterion is examined in Figs. 4 and 5. The AMSR-E T89H image (lower panel of Fig. 4) shows the obvious difference between land and water (colder TBs over water); this is why the special steps to identify open ocean and clear coast are necessary, because most ocean TBs are below 257 K, while those with rain have higher TBs than the surrounding ocean. The results for the footprints that are classified as possible rain from the Fig. 3 decision tree are shown in the T21V versus T85H scatterplot of Fig. 5. The majority of the T85H values of Fig. 5 are greater

FIG. 4. Example of an instantaneous rainfall field from 1000 UTC 12 Apr 2003, using the HA93 screening, compared to ground-based radar validation data. FIG. 5. TMI brightness temperature distribution for collocated TRMM PR rainfall rate estimates using a sample of summer 2003 South American coastal data.

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The scatterplot of PC values in Fig. 6 appears to give a more clear separation between rain and no-rain footprints than Fig. 5, with the cutoff shown by the line. The only question is the choice of cutoff threshold; while better than the 85-GHz TB cutoff, Fig. 6 does not indicate a distinct separation between rain and no-rain footprints. So another, more traditional variable that has been used over coast, the polarization corrected temperature (PCT) of Spencer et al. (1989), is tested.

d. Proposed PCT cutoff threshold The PCT is very useful for coastal rainfall estimation because it is fairly insensitive to the underlying surface (land or water), so that the high-frequency PCTs respond almost exclusively to the ice scattering signature. Spencer et al. (1989) used the following expression for the 85-GHz SSM/I PCT: PCT ⫽ 共␤T85H ⫺ T85V兲Ⲑ共␤ ⫺ 1兲. FIG. 6. Principal components derived from African coast data applied to collocated TMI and TRMM PR footprints for the South American coast.

than 257 K, because this is part of the first criterion of the decision tree (Fig. 3). As suggested by the image of the T85H field in Fig. 4, T85H does not provide a clear cutoff; Fig. 5 shows many raining points with T85H ⬎ 257 K and many nonraining points with T85H ⬍ 257 K. The abundance of TMI TBs with collocated TRMM PR rain estimates facilitates investigating other cutoff criteria that may give better separation between the raining and nonraining footprints, as indicated by the TRMM PR.

c. Proposed PCA cutoff threshold Due to the high correlation between the different microwave channels (nine for TMI and nine that are used for AMSR-E rainfall estimation), principal component analysis (PCA) is often useful to isolate the signals of geophysical variables by creating eigenvectors that are mutually orthogonal, that is, statistically uncorrelated, to one another (Conner and Petty 1998). The PCA was performed on the complete 4-month TMI dataset as well as on subsets, because the results varied from sample to sample. One subset of African coastal data yields principal components that do a fair job of separating rain from no-rain footprints, as shown in Fig. 6. From images of the principal components (not shown), the second principal component (PC2) seems to represent the surface temperature that is modified by rainfall; the oceanic PC2 values are much lower than the land values, but rain increases the value of PC2 over both surfaces. The fourth principal component (PC4) responds in an opposite manner—PC4 values are higher over ocean than over land, and rain lowers the value of PC4 over both surfaces.

共2兲

The optimal value for the relative weighting ␤ between the vertical and horizontal polarizations is somewhat uncertain; Spencer et al. (1989) used ␤ ⫽ 0.45 to give PCT values in a desired range from 275 to 290 K, but this should be reexamined for TMI and AMSR-E. The first task here is to determine appropriate values for ␤ and the cutoff threshold, again using the wealth of new collocated radar and radiometer data. Figure 7 illustrates the potential for separation of raining from no-rain classifications using the 85-GHz PCT with ␤ ⫽ 0.45, as in Spencer et al. (1989); there is better separation using the PCT than with the T85Honly or PCA results shown previously. The PCT is plotted against T21V, because T21V can be used as a proxy for the surface temperature that would be seen by the higher frequencies (Grody 1991), because T21V responds to atmospheric moisture, which may have a similar physical temperature as the surface and is not as sensitive to surface emissivity variations. For higher values of T21V, which are presumably due to a warmer surface, the TRMM PR shows more rainfall for a given PCT. Varying ␤ (not shown) does not change the locations of the points substantially; for different ␤ values the best separation between the raining and nonraining footprints appears to be a 1-to-1 line, but with slightly different intercepts. The line has the form T21V ⫽ PCT ⫹ T,

共3兲

where Y is the intercept and the rain points fall above the line, so that lower Y values result in more footprints classified as rain. So, in addition to ␤, the optimal intercept Y should be determined. The best choice of Y appears to be between 3 and 5; these three lines are shown in Fig. 7. The two criteria used to determine the value of ␤ are 1) visual inspection of the PCT fields, looking for relatively smooth transitions between water and land; and 2) a quantitative measure [Heidke skill score (HSS)] of

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FIG. 7. PCT using ␤ ⫽ 0.45 derived from Jul 2003 South American coastal data applied to collocated TMI and TRMM PR footprints.

the correspondence between the radar and radiometer rainfall fields. The same TRMM data (May–August 2003 for the coasts of South America, Africa, and Australia) used to create Table 3 are used to calculate the HSS, a quantitative measure between ⫺1 and 1 based on yes/no counts for two variables. Here the counts are for both rain, both no-rain, only PR rain, and only TMI rain. A value of 1 would signify perfect correspondence between the two fields. First, examples of radar fields (upper panels) and PCT (lower panels) are plotted in Fig. 8 using both the Eureka radar and TRMM PR. There is no perfect ␤ value that yields smooth transitions everywhere; however, ␤ ⫽ 0.4 results in smooth transitions for the majority of cases. A higher ␤ value of 0.5 (not shown) tends to produce higher PCTs over the ocean, whereas a lower ␤ value of 0.3 (also not shown) results in higher PCTs over land. The PCTs over Eureka (upper-right image) have a smooth transition from water to land, and the lower PCT values correspond to the radarrainfall areas. The TMI PCTs over the northern coast of South America (lower right) show slightly higher PCTs over land in the upper-right portion of the figure. But for the subsequent overpass of the southwest coast of South America (not shown), the PCTs are higher over water, indicating the case-to-case variability. So, based on the continuity criterion, ␤ ⫽ 0.4 is the best option. Table 4 presents the results of a quantitative sensitivity study that is used to select optimal values of ␤ and intercept Y. The HSS does not change significantly over the range of possible values. Depending on the intercept, the optimal ␤ ranges from 0.3 to 0.4, close to the desired range based on visual inspections, such as with Fig. 8. So, based on this sensitivity study in conjunction with images, ␤ ⫽ 0.4 and Y ⫽ 3 are chosen as the optimal parameters for the PCT cutoff threshold.

e. Overall quantitative improvements Both the nonzero rainfall rate retrievals for previously ambiguous footprints, and implementation of the PCT-based rain identification procedure improve the rain detection with respect to TRMM PR rainfall. Figure 9 presents the zonal statistics for rain fraction and HSS for both South America and Africa for May– August 2003. For most latitudes of both continents, the HSS improves significantly for each of the two upgrades to the algorithm. The TMI still underestimates the TRMM PR rain area; interestingly, the modifications result in much more rain between 10°S and 20°N, but almost no more rain for latitudes farther from the equator. It is not clear why the upgrades produce better results around the equator; perhaps the PCT method works much better for convective rainfall found around the equator, because the PCT responds to scattering found in convective rainfall, whereas the previous 85-/ 89-GHz cutoff does not isolate the scattering signature to the same extent. Overall, the TMI rain fraction is still significantly low; while it increases from 0.015 to 0.023, it is still much less than the TRMM PR rain fraction of 0.052. But, while for some areas the rain fraction does not increase significantly, the method is identifying more correct rain footprints and fewer incorrect ones, as evidenced by the HSS increases. The higher HSS values indicate that the increased rainfall area does not come at the expense of excessive false alarms.

4. Application to AMSR-E data While the results of the new screening procedure are encouraging for the tropical regions over which they were developed with TRMM PR and TMI, it is necessary to test the new procedure at higher latitudes, par-

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FIG. 8. Example (left) TMI PCT fields for ␤ ⫽ 0.4 compared with the Eureka, CA, ground-based radar-rainfall for 1000 UTC 12 Apr 2003, and (right) TRMM PR spaceborne radar rainfall over the northern coast of South America from 1700 UTC 3 Aug 2003.

ticularly because this algorithm will also be used for the polar-orbiting AMSR-E and possibly future polarorbiting sensors as part of the GPM constellation. The ambiguous classification of HA93 was developed primarily for melting snow, which is infrequent inside the TRMM domain. Plus, it is much more difficult for microwave algorithms to detect the nonconvective rainfall that is often found outside the Tropics. For these reasons, the U.S. AMSR-E team has devoted resources to

producing rain-rate maps from two rainfall validation sites outside the TRMM domain, including 1) radarrainfall maps from the coastal radar of Eureka, and 2) gauge-adjusted, gridded radar-rainfall estimates for the Baltic Sea region [Baltic Sea Experiment (BALTEX)]. But, the first step for testing the algorithm modifications developed with TRMM data is to observe global AMSR-E images to determine whether there are any obvious misclassifications.

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TABLE 4. Sensitivity of Heidke skill score to ␤ and intercept Y for the PCT thresholding method.

␤ (down)/ intercept (across)

2

3

4

5

0.25 0.3 0.35 0.4 0.45 0.5

0.418 0.427 0.433 0.437 0.440 0.439

0.440 0.445 0.447 0.447 0.446 0.443

0.445 0.447 0.447 0.446 0.444 0.440

0.442 0.443 0.442 0.440 0.436 0.431

Global observation The global images (not shown) of mean daily rain rate from several consecutive days of February 2004 were observed. As suspected, many of the coastal footprints with ambiguous classifications 63–65, which have positive TRMM PR rain in most tropical cases (Table 3), are incorrectly classified as rain for cold season cases. Some large lakes in northern latitudes are misclassified consistently when classifications 63–65 are assigned a positive rain value, so a modification to the algorithm is needed to eliminate these false signatures. Many of the misclassifications occur near the TB cutoff values of the decision-tree method (Fig. 3). In particular, many footprints that should not have rain are assigned ambiguous values of 63 or 65 when T24V is slightly greater than 0.88(T19V) ⫹ 37.9. Grody (1991) determined this threshold using SSM/I data, so it may not apply directly to AMSR-E data. After testing different thresholds for AMSR-E data, it was determined that T24V ⬎ 0.88(TB19V) ⫹ 51 is sufficient for retrieving a positive rain value for a footprint assigned the ambiguous classifications 63–65. Otherwise the footprint should be left as ambiguous. This appears to eliminate most of the cold season coastal footprints that were incorrectly identified as rain. In addition, the Grody (1991) threshold giving the ambiguous class 64 from Fig. 3 occurs when T24V ⬎ 261.9 K or T24V ⬎ 163.3 ⫹ 0.49(TB89H). A new threshold for AMSR-E that eliminates false rain signatures is 192 ⫹ 0.49(TB89H). Using these new findings, the class 63–65 ambiguous footprints, satisfying both the modified criteria, TB24V ⬍ 0.88共TB19V兲 ⫹ 51

共4兲

TB24V ⬍ 192 ⫹ 0.49共TB89H兲

共5兲

are now left as ambiguous (i.e., no estimates are assigned to these footprints), and the class 63–65 footprints not meeting these extra criteria are assigned positive rainfall values, which are subject to further testing as described below. This modification is for removing false rain signatures, but it could be at the expense of removing legitimate rain footprints. To quantify the impact of these removals, collocated TRMM PR and TMI footprints for the African, South American, and Australian coasts

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for a 3-month period of December 2003 through February 2004 are used. Table 5 gives TRMM PR rain percentages as in Table 3, but for the different time period. Compared to Table 3, the TRMM PR rain percentage is lower (87.8% compared to 92.2%) for class 64, but is higher (85.7% compared to 82.8%) for class 65. Removing the data based on Eqs. (4) and (5), and not including the ambiguous classification 66 that is always left as ambiguous, results in the values of Table 6. For these tropical data, Table 6 shows that most of the ambiguous footprints of Table 5 remain ambiguous, so the benefit of changing ambiguous footprints to rain footprints is small for the TRMM region. Observations of global AMSR-E images (not shown) using the modified ambiguous classification procedure indicate that most of the false signatures resulting from classes 63–65 are removed. However, there still are false signatures in cold regions due to the first step of the decision-tree method of Fig. 3, which was developed by HA93 as criteria for a no-rain classification, but was changed later to a criteria for possible rain for TMI, because it only appears to result in false rain signatures for cold regions. But global observations indicate that it should be reinstated as a criterion for no rain. The reinstated criterion resulting in no rain, indicated in italics in Fig. 3, is T85H ⬎ 257, T22V ⬍ 269.1.

共6兲

The collocated TRMM PR/TMI data for December 2003 through February 2004 are used to test the effects of changing the Eq. (6) criterion (for TB21V). It appears that over the Tropics the effect is small, because only 69 footprints that are previously identified as having rain are removed, still leaving 16 732 85-GHz footprints with estimated rain greater than zero. Of these 69 footprints, 65 have positive TRMM PR rainfall, which is possibly the reason why the condition was changed in the TMI code to a condition for positive rain. So here, this change is made for AMSR-E only, because the cold season false rain is not a problem for TMI and there seems to be no need to remove any TMI footprints; it even appears detrimental to remove these TMI footprints because they have mostly positive TRMM PR rainfall. The zonal profiles of the footprint fractions left as ambiguous or changed to a positive rain rate for both TMI and AMSR-E (Fig. 10) indicate that many more footprints changed to rain for AMSR-E compared to TMI after application of Eqs. (4)–(6). The upper panel shows the fractions of rain footprints in each of the following three categories: the fractions classified as before as rain, the new rain fractions resulting from adding the footprints previously left as ambiguous, and the fractions still left as ambiguous after the changes. Consistent with Tables 5 and 6, the fraction of footprints that are changed to rain is very small for TMI. However, for AMSR-E more than half of the ambiguous

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FIG. 9. Zonal profiles of rain fraction and HSS for (a) South America and (b) Africa, using all collocated TMI and TRMM PR coastal data, May–Aug 2003.

tropical footprints are changed to rain even after application of Eqs. (4)–(6). The reason for the difference is not completely clear, but it appears to be due to a difference in TB distributions between AMSR-E and TMI (Fig. 11). Due to the higher resolutions at all frequencies, and more so at the lower frequencies (Tables 1 and 2), AMSR-E histo-

grams have more of a bimodal distribution, with the lower peaks for water footprints and the higher peaks for land footprints. TMI has more footprints with land– water mixtures. Possibly, these differences between TMI and AMSR-E are enough to give different fractions of classifications by the decision-tree method. The additional steps of Eqs. (4)–(6) appear to limit

TABLE 5. Results for ambiguous footprints for African, South American, and Australian coasts, Dec 2003–Feb 2004 using the decision tree of Fig. 3. Classification

Situation

No. of footprints


63 64 65 66

Significant scattering over water Significant scattering over lakes Less scattering over water Less scattering over land

885 360 2916 1138

99.1 87.8 85.7 5.5

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TABLE 6. Results for ambiguous footprints for African, South American, and Australian coasts, Dec 2003–Feb 2004 using the decision tree of Fig. 3 with Eqs. (4) and (5). Classification

Situation

No. of footprints


63 64 65

Significant scattering over water Significant scattering over lakes Less scattering over water

867 334 2354

99.1 87.1 87.8

the addition of valid rainfall from the Tropics more for TMI than for AMSR-E, but fortunately there are not significant problems with cold season misclassification with TMI data, so these modifications may not be necessary for TMI. The only places in the TMI domain with false rain signatures resulting from retrieving positive rainfall for classes 63–65 appear to be at high altitudes, where rainfall retrievals often have difficulties, and high latitudes, where TRMM may observe some cold season conditions. To eliminate this problem, the elevation screen in the land component of the algorithm (Kummerow et al. 2001) was added to the coastal screen, so that any footprints at elevations over 2 km are left as ambiguous as in the land component. To

correct the high-latitude problem, Eqs. (4)–(6) are applied outside of 30°N–30°S. All of these modifications result in the AMSR-E zonal profiles shown in Fig. 10. The changes to the algorithm give the intended results of more tropical rainfall without adding much rain in the high latitudes where there is the potential for false signatures. Finally, Fig. 12 shows the mean global rainfall for those coastal footprints used to calculate the zonal profiles of Fig. 10. The false signatures at the high latitudes are removed and the global field seems realistic. In summary, the following additional changes were made to the AMSR-E algorithm. 1) All ambiguous footprints with elevations greater than 2 km remain ambiguous. 2) All ambiguous class 63–65 footprints between 30°N and 30°S are assigned their positive rain values as long as their elevations are less than 2 km. 3) The initial criterion of the decision tree [Eq. (6)] is changed from a possible rain condition to a no-rain condition. 4) The new criteria of Eqs. (4)–(5) are applied after the decision tree to convert some of the footprints that were previously classified as ambiguous classes 63– 65 to rain. The modifications to the HA93 decision tree are also shown in Fig. 3. These changes were made for the submission of the AMSR-E algorithm to be released to the public on 1 September 2004, but after submission of the TMI algorithm to be released to the public in 2004. The submitted TMI algorithm simply retrieves positive rainfall rates for ambiguous classifications 63–65 based on section 3; this could result in some false rain signatures at the highest TRMM latitudes and high elevations.

5. Validation a. Eureka, California, radar

FIG. 10. TMI and AMSR-E zonal profiles for three different outcomes of the classification procedure: 1) originally rain possible, 2) originally ambiguous changed to rain possible, and 3) footprints left as ambiguous. Data are from 3 Jan, 17 Jan, 31 Jan, and 10 Feb 2003.

The existing Next Generation Weather Radar (NEXRAD) coastal radar in Eureka, California, was chosen as an AMSR-E validation site because it is the NEXRAD radar that is nearest to the coast and is at high latitudes where coastal rainfall estimation is more difficult, particularly in the cold season, due to nonconvective rainfall without as much cloud ice. Radarrainfall maps are produced routinely at high spatial and temporal resolution for comparisons between the

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FIG. 11. TMI and AMSR-E TB distributions of coastal footprints from Africa, South America, and Australia. TMI footprints are from Dec 2003 to Feb 2004, AMSR-E footprints are from 24 Feb 2004.

AMSR-E products and surface rain rate. The positive rain rates from AMSR-E overpasses during the 2003 rainy season are compared with the radar rain rates for each case to compare satellite-derived and radarderived fields. It appears to be a difficult area to correctly capture the rain, because when the modifications were made to eliminate the false rain signatures at high latitudes, ambiguous areas where the Eureka radar identifies rain were changed from rain back to ambiguous. So the final results look very similar to the image of Fig. 4, which shows large areas without estimates where the ambiguous classification is made. Other than these ambiguous areas, the patterns tend to match reasonably well, although in the case of Fig. 4 and most cases, regions with the lightest radar-rainfall category (0–1 mm h⫺1) are usually classified as no rain. This is why the overall rain area is underestimated by

more than a factor of 2, as stated previously. With the matched TMI and TRMM PR dataset from May to August 2003, the TRMM PR rain fraction over 1 mm h⫺1 is just 0.020, compared to the 0.052 fraction greater than zero. This means that over one-half of the TRMM PR rain is between 0 and 1 mm h⫺1 for this dataset of TMI footprint-averaged rainfall rates. As in McCollum and Ferraro (2003) for the SSM/I, TMI, and AMSR-E land algorithms, estimation of very light rain is not attempted, because it is very difficult to detect with the highest microwave frequencies of 85/89 GHz on these three instruments, and attempts to estimate light rainfall tend to reduce the HSS, as explained previously. The AMSR-E rain-rate images of this study show that most of the microwave-estimated rainfall rates are above 2 mm h⫺1. McCollum and Ferraro (2003) show that while not having an overall bias, the

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FIG. 12. Mean global coastal rainfall from the modified algorithm for 30 Jan, 17 Jan, 31 Jan, and 10 Feb 2003.

TMI algorithm tends to overestimate the very low rain rates (to make up for the low probability of detection of light rain and avoid an overall low bias) and underestimate the high TRMM PR rain rates, not attempting to estimate extremes in order to maximized the HSS. Once rain is identified, the rain-rate retrieval over the coast is similar to that over land, so the coastal rain-rate error statistics should be similar to those of the land algorithm.

b. BALTEX The gauge-adjusted radar-rainfall dataset from the Baltic Sea region, produced for the BALTEX validation campaign, is useful for this study because they are for even higher latitudes (50°–70°N) than the Eureka data and may contain snow- and ice-covered surfaces, or at least cold season nonconvective rainfall, which are all difficult conditions for coastal rain retrievals. As with the Eureka data, many AMSR-E overpasses of rain events were used to create comparisons of rainrate images between the satellite and radar. The trends from the images for October 2002 and January 2003 show similar results as the Eureka data. If all of the ambiguous footprints are assigned positive rain rates,

the AMSR-E estimates match the gauge-adjusted radar rain rates well, but when Eqs. (4)–(6) are applied to eliminate the incorrect rain estimates in cold regions, the results are virtually unchanged from the original algorithm. Quantitative comparisons between all AMSR-E footprint rain estimates and collocated gauge-adjusted radar-rainfall estimates are shown in Tables 7–9. The data are from nearly 1 million match-ups throughout October 2002. Table 7 presents fractions of the total number of AMSR-E footprints with rainfall estimates of either greater than zero or zero for radar and AMSR-E, plus ambiguous AMSR-E footprints. For both radar and AMSR-E, 73% of the footprints have rain estimates of zero, and 26% of the footprints have TABLE 7. Rain/no-rain/ambiguous contingency table for rain fractions out of 978 894 AMSR-E footprint estimates with collocated gauge-adjusted radar-rainfall estimates for the BALTEX region, Oct 2002. Fraction of total footprints

AMSR ⫽ 0

AMSR ⬎ 0

AMSR ambiguous

Radar ⫽ 0 Radar ⬎ 0

0.73 0.26

0.00045 0.00021

0.0046 0.0056

MAY 2005

TABLE 8. Monthly AMSR-E rain contributions for the 978 894 AMSR-E footprint estimates of Table 7, classified by the collocated radar-rainfall rain rates. Monthly AMSR rain rate (mm)

AMSR ⬎ 0

AMSR ambiguous

Radar ⫽ 0 Radar ⬎ 0

1.0 0.34

10.3 12.2

AMSR-E rain estimates of zero but positive radarrainfall estimates; most are very light (⬍1 mm h⫺1) rates. Light rain rates usually do not produce much ice scattering, so the microwave algorithm does not detect them. Less than 0.001% of the footprints have estimates greater than zero in the AMSR-E algorithm, and approximately 0.01% of the AMSR-E footprints are classified as ambiguous. About half of these footprints are associated with positive radar rainfall and about half with zero rainfall. So while the probability of detection is very low, assigning positive rain rates to the ambiguous footprints would give a high false alarm ratio. So, the AMSR-E algorithm has poor performance in classification of these light rain rates. The mean monthly rain rates for the region for AMSR-E and gauge-adjusted radar rain rates are shown in Tables 8 and 9, respectively. If the ambiguous AMSR-E footprints were assigned their positive rain rates, the mean monthly rainfall from AMSR-E (⬃24 mm month⫺1, obtained from adding the four numbers in Table 8) would be close to that from the radar (⬃28 mm month⫺1), because the algorithm is designed to compensate for the low probability of detection by microwave by assigning higher conditional rain rates, so that the overall mean rain rate is relatively unbiased. These mean monthly rain rates (less than 30 mm month⫺1) are very low because of the primarily very light rain rates for the region and month. The best solution to this problem of rain identification at high latitudes is probably the use of frequencies much higher than the 85- and 89-GHz channels on SSM/I, TMI, and AMSR-E; results from the 150-GHz channel on the Advanced Microwave Sounding Unit (AMSU)-B have shown promise in this area (Bennartz and Bauer 2003; Weng et al. 2003).

6. Summary and conclusions The GPROF coastal rainfall identification procedure is updated for TMI and AMSR-E from the method derived by HA93 for SSM/I, because this previous method may not be optimal for TMI and AMSR-E due TABLE 9. Monthly gauge-adjusted radar-rainfall contributions for the 978 894 AMSR-E footprint estimates of Table 7, classified by the collocated AMSR-E footprint estimates.

Monthly radar rain rate (mm)

511


AMSR ⫽ 0

AMSR ⬎ 0

AMSR ambiguous

24.3

0.087

3.6

to their superior resolutions to SSM/I, in addition to the slightly different frequencies. The previous decisiontree method to classify coastal footprints as possible rain, no rain, or ambiguous is left in place. The two major additions to the method are 1) the ambiguous classifications of 63, 64, and 65, which are now classified as rain for appropriate conditions; and 2) for the rain possible footprints, a PCT-based cutoff threshold is used instead of the straight TB threshold of the previous algorithm. To avoid erroneous positive rain-rate retrievals for nontropical no-rain situations, additional TB thresholds plus elevation and latitude cutoffs were determined so that rainfall is added with respect to the previous algorithm, while minimizing the incorrect rain identifications. The new method is developed primarily using collocated TRMM PR and TMI data, assuming that the TRMM PR identification is correct and finding the TBbased method that best matches the TRMM PR– estimated rain fields. The modifications based on TRMM data are tested with AMSR-E data and several adjustments are needed to ensure that false rain signatures for high latitudes are minimized. The new method has poor skill in identifying the lightest rainfall rates below 1–2 mm h⫺1, but the portion of the rainfall field that is greater than 2 mm h⫺1 has reasonable correspondence with ground-based radar-rainfall estimates. This work was done specifically for the TRMM version 6 and AMSR-E version 1 algorithms, which were released to the public in 2004. Further testing needs to be done to apply these changes to SSM/I; the results may differ because of the larger footprint sizes. Future work on the GPROF coastal component will deal further with the use of PCT to estimate rainfall, particularly for the estimation of rainfall rates, because this study deals with identification only and uses existing methods for rainfall rate estimation. This work in microwave coastal rainfall estimation has great practical importance in applications that affect people and property; for example, these coastal rainfall estimates are used directly in some prediction methods for landfalling rainfall from tropical cyclones (Ferraro et al. 2005; Kidder et al. 2005). Results show that the method developed in this study increases the estimated rainfall in tropical coastal regions and gives better correspondence with radar compared to the previous algorithm version, which should result in improved coastal rainfall predictions. Acknowledgments. The first author was supported by NASA Grant S-87398-F. Ralf Bennartz supplied the BALTEX radar-adjusted rainfall rates, the software to read them, and other useful information. REFERENCES Adler, R. F., A. J. Negri, P. R. Keehn, and I. M. Hakkarinen, 1993: Estimation of monthly rainfall over Japan and surrounding

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