VALIDATION AND ANALYSIS OF PRECIPITATION EXTREMES IN THE TMPA

G.J. Huffman1,2, R.F. Adler1,3, D.T. Bolvin1,2, E.J. Nelkin1,2

1: NASA/GSFC 2: Science Systems and Applications, Inc.

3: Univ. of Maryland/ESSIC

Abstract

We compute several standard indices of precipitation “extremes” using the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) product and compare their performance with a previously computed set of station-based results. Specifically, annual-total precipitation is generally well correlated, except for a few stations with demonstrated deficiencies in the satellite data. The annual amount of precipitation that falls on days exceeding the 95th percentile of days is moderately correlated, while the runs of consecutive dry days are only modestly correlated. This successive decrease in correlation is consistent with increased sensitivity to the details of satellite-gauge relationship, including the point-area spatial mismatch. 1. The TMPA The international constellation of precipitation-sensing satellites presents the community with a diverse set of individual precipitation estimates characterized by various periods of record, regions of coverage, and sensor-specific strengths and limitations. The goal of combination analysis schemes is to combine these input estimates into a “best” data set. TRMM computes combination products as a standard part of the TRMM data. In particular, the TMPA is computed in near-real (RT) and post-real (currently Version 6) time. Both TMPA products are computed on a 3-hour, 0.25° grid as follows. All microwave input fields are intercalibrated to a standard and combined, centered on the nominal observation times (00, 03, …, 21 UTC), with priority given to conically scanning microwave instruments. The Version 6 calibration standard is the TRMM Combined Instrument product (2B31), while the RT calibration standard is the GPROF-TMI product (2A12) because 2B31 is not computed in real time. Then the combined microwave is used to calibrate the infrared (IR) Tb’s to precipitation using months of coincident data. Finally, the combination is composed of microwave estimates where available and IR estimates for all other grid boxes (product 3B42RT for the RT). In the post-real-time product, an additional step is taken – the 3-hourly satellite fields are accumulated for the month, then combined with monthly precipitation gauge analyses (product 3B43), and all individual 3-hourly fields are rescaled to approximately sum to 3B43, yielding product 3B42 Version 6. See Huffman et al. (2007) for more details, and http://precip.gsfc.nasa.gov for technical documentation and freely available data access.

Users are urged to focus on the post-real-time product fwhenever possible, which is uniformly processed from 1998 to the present. The real-time product is designed for monitoring purposes and has known limitations. The current real-time processing scheme has been applied only since February 2005. At the time of the Workshop the next RT version was late in beta testing, with anticipated release in early February 2009. Version 7 of the post-real-time product will be released late in 2009 or early in 2010, featuring additional satellite data sources, a new gauge analysis, and full reprocessing of the entire record starting in 1998. 2. Climate-Oriented Indices of “Extreme” One interesting application of such a long, uniform dataset is to estimate the extreme behavior of precipitation around the globe. The short record involved makes it impractical to apply sophisticated curve-fit estimates of extreme events. Rather, the authors chose to adopt a set of standards for defining “extreme” to facilitate conversations with the climate community. Specifically, the CCl/CLIVAR/JCOMM Expert Team (ET) on Climate Change Detection and Indices (ETCCDI) had been charged with developing and promoting such definitions. They defined 11 “core indices” for precipitation and provided the corresponding station time series for a large number of countries (posted at http://cccma.seos.uvic.ca/ETCCDI/data.shtml). Unfortunately, their procedure did not extend to posting the respective climatologies. Note that a “rainy day” is defined as an accumulation of at least one millimeter in a day. Also, the data are at stations which are too sparse to create gridded data, so the comparisons will be somewhat hampered by the point-area spatial mismatch. In this study we chose to examine three of the ETCCDI indices: 23. CDD Maximum length of dry spell 25. R95pTOT Annual total PRCP when RR > 95p 27. PRCPTOT Annual total precipitation To the extent possible, we wished to examine both wet and dry indices and avoid excessive sensitivity to artifacts in both the gauge and satellite data sets. 3. 40°N-S: Results and Issues The full time-span of available satellite data covers 10 years, 1998-2007, which is certainly short for developing a truly climatological perspective. As well, the satellite data cover only the latitude band 50°N-S. Within these caveats, the satellite results are shown in Fig. 1. PRCPTOT is just the familiar grand-average climatology. The 95th percentile precipitation looks fairly similar, but there are important differences. Examples include an additional maximum off the Pacific coast of Mexico and a reversal of the overall gradient between Amazonia and the de la Plata region in South America. The runs of consecutive dry days tends to be anti-correlated to PRCPTOT, but differences include a nearly flat average CDD field in the SPCZ, where PRCPTOT shows a significant North-to-South gradient. Note that blacked-out areas had rain events in less than half the years.

Turning to comparisons with the ETCCDI data, there were only six years of overlap, 1998-2003, so we required that the stations used in the comparison have data in all six years. As well, there are some known issues with the TMPA during the first two years at latitudes outside the band 40°N-S, so the stations were limited to that zone. A total of 593 stations were used, and their positions are plotted on the PRCP map at the top of Fig. 1. The black dot denotes the grid box with at least (and typically) one station, with a red “halo” to better mark the location. These stations cover a reasonable range of climate zones, but miss the areas with the highest rain rates. A programming issue

Fig. 1 Annual averages computed over 1998-2007 with the TMPA V.6 for precipitation (top), precipitation on days whose precipitation exceeds the 95th percentile amount (middle), and longest run of consecutive dry days (bottom). Grid boxes with at least one station are plotted as black dots surrounded by a red “halo” on the top panel. Average runs of dry days greater than two years are depicted with black in the bottom panel.

PRCP (mm/d)

R95p (mm/d)

100 200 300 400 500+ CDDavg (days)

prevented displaying the full global comparison at the 4th IPWG, but it was subsequently resolved and they are displayed in this paper. The first comparison is for annual precipitation. As shown in Fig. 2 (top), the TMPA and gauge values are generally well correlated. The principal exceptions are for stations whose six-year averages differ by at least a factor of two. The corresponding annual values are the red and orange dots in Fig. 2 (top). The 12 stations for Bhutan crystallize this issue; the four gauge stations along the country’s southern border have very high values compared to the satellite (orange dots), while the remaining stations to the north are reasonable (green dots). It turns out that the gauge analysis on which the TMPA depends, provided by the Global Precipitation Climatology Centre (Deutscher Wetterdienst), lacks any stations in this high-precipitation region along the southern foothills of the Himalayas, and so cannot be expected to capture these high values. The time series correlation at individual stations (Fig. 2 [bottom]) tends to be better than 0.8, with the large-difference stations (red dots) showing lower correlation. The negative correlations at low gauge precipitation rates are still under study. The second comparison is for precipitation in excess of the 95th percentile (R95pTOT; Fig. 3). The scattergram (Fig. 3 [top]) has more spread, and the individual station-years for the “bad” stations are not as well separated as in the annual precipitation case. In general, the R95pTOT (Fig. 3 [bottom]) is relatively unbiased on a global basis, although the correlations are not as high. Fig. 4 shows that the relationship between PRCPTOT and R95pTOT is nearly the same for both the gauges and the TMPA. The linear fit lines are very close and there is a slight non-linearity, with an excess of points above the fit lines around PRCPTOT values of 1500 mm. The third comparison is for the maximum run of consecutive dry (i.e., < 1 mm) days in a year (CDD; Fig. 5). The scattergram (Fig. 5 [top]) has even more spread, with no clear delineation between the red and black dots. Correlations (Fig. 5 [bottom]) are low, as befits a statistic that depends strongly on the detailed time series of precipitation events. 4. Concluding Remarks The work here has shown an initial computation of “extreme” statistics from the TMPA, a state-of-the-art high-resolution precipitation product. The statistics were chosen to match existing definitions of “extreme” so that we could make a comparison with previous work. They compare relatively well, but the parameters based on rarer events have lower correlation. In general, the TMPA-based R95pTOT and CDD tend to be slightly higher than the gauge values. Future work will focus on identifying a satellite-based index that predicts poor performance for each parameter. As well, we plan additional comparisons using gridded gauge analyses, such as the Climate Prediction Center analysis of the daily cooperative observer data over the United States. References Huffman, G.J., R.F. Adler, D.T. Bolvin, G. Gu, E.J. Nelkin, K.P. Bowman, Y. Hong, E.F.

Stocker, D.B. Wolff, 2007: The TRMM Multi-satellite Precipitation Analysis: Quasi-Global, Multi-Year, Combined-Sensor Precipitation Estimates at Fine Scale. J. Hydrometeor., 8(1), 38-55.

Fig. 2. (top) Scattergram of TMPA vs. gauge annual precipitation (PRCPTOT; in mm). Orange and red dots are for stations in Bhutan and in the rest of the countries, respectively, for which the six-year average precipitations differ by at least a factor of two. Green and black dots are for stations in Bhutan and other countries, respectively, that do not have this problem. (bottom) Time-correlation for all stations; red dots are all stations with average PRCPTOT’s differing by more than a factor of two.

Fig. 3. (top) Scattergram of TMPA vs. gauge annual precipitation for events greater than 95th percentile (R95pTOT; in mm). (bottom) Time-correlation for all stations. For both panels, red dots are all stations with average R95pTOT’s differing by more than a factor of two.

Fig. 4. Scattergram of annual precipitation for events greater than 95th percentile versus annual precipitation (in mm) for gauges (black) and the TMPA (red). The linear best-fit lines for the gauge and the TMPA are green and orange, respectively.

Fig. 5. (top) Scattergram of TMPA vs. gauge annual maximum run of consecutive dry days (CDD; in days). (bottom) Time-correlation for all stations. For both panels, red dots are all stations with average CDD’s differing by more than a factor of two.