In the east Bay of Bengal (BoB), the precipitation maximum always lies near the eastern coast on the windward side of Mountain Araka Yoma in the summer monsoon season. In this study, different precipitation products are compared in terms of their representation of the offshore rainfall maximum feature in this region. Climatologically, all products examined present similar rainfall distribution except for the CMAP. Significant discrepancies among different products are found in the interannual variation, as illustrated by the contrast features between 2002 and 2005. Based on the TRMM products (except for 3B42RT) and GPCP V1.2, the precipitation maximum occurred near the coast in 2002, while it was about 100–200 km offshore in 2005. However, this difference is not obvious in the GPCP V2.2 and TRMM 3B42RT products. Larger easterly vertical wind shear and warmer SST were present in 2005. Both favor stronger orographically-forced convective systems to propagate offshore, leading to the offshore rainfall maximum in 2005. Therefore, it is suggested that the TRMM 3B40RT, which is mainly based on passive microwave estimates, may be more reliable among different precipitation products in reflecting the precipitation feature in the coastal region of the east BoB.
Precipitation is one of the most important climate variables in the study of climate variability and change and also a major component of the earth’s water and energy cycles. Reliable precipitation dataset with high spatial and temporal resolution is the key to the understanding of climate and climate variability and the verification of numerical weather prediction and climate simulations. Precipitation is also an important input variable for ocean circulation models as freshwater flux and an input variable for many other application models, such as land surface models and hydrometeorological models. Numerous global precipitation datasets have been developed in the last two decades or so using different data sources, such as in situ observations, satellite estimates, climate model simulations, and/or their combinations.
Because of the lack of sufficient in situ observations, current precipitation estimates over the open oceans are heavily dependent on satellite retrievals, such as the Tropical Rainfall Measuring Mission (TRMM) satellite. Because of the uncertainties in different retrieval algorithms and the lack of true observations to calibrate the estimates, precipitation products from different data sources show a certain degree of discrepancies, which sometimes are quite large over the tropical and subtropical oceans (e.g., [
The TRMM is a joint US-Japan satellite mission to monitor the tropical and subtropical precipitation and to estimate its associated latent heating. The TRMM is the first satellite from which its observations are able to provide detailed and comprehensive datasets on the four-dimensional distribution of rainfall and latent heating over vast tropical and subtropical oceans and continents. It has been widely used in many earth science applications, such as global drought and flood monitoring [
It is our interest to compare the different precipitation products in the east Bay of Bengal (BoB) in the Asian summer monsoon season. This is motivated by the importance and the special feature of precipitation in this region. The east BoB is one of the sites with the highest precipitation during summer in the Asian monsoon domain. As the southwesterly monsoon flow impinges on the narrow mountain range of Araka Yoma in Myanmar coast, moist air is forced to rise by the orographic lifting [
In this study, different precipitation products are used to document the variation of precipitation in the eastern coastal region in the BoB. The offshore feature of precipitation on the windward side of the mesoscale mountain range in Myanmar is the main focus. We will discuss the discrepancies among different precipitation products in reproducing those features. Because of the lack of in situ observations, we will evaluate the precipitation distribution based on physical reasoning with the large-scale circulation difference that is dynamically responsible for the precipitation features. The rest of the paper is organized as follows. The datasets are described in Section
The precipitation products evaluated in this study include three major products, namely, TRMM products (3B40RT, 3B41RT, 3B42RT, and 3B43), GPCP products (versions 1.2 and 2.2), and CMAP. The precipitation datasets and several other data used in our analysis are described below briefly.
The TRMM satellite measures rainfall in tropical and subtropical regions by use of the following instruments: precipitation radar (PR), TRMM microwave imager (TMI), and the visible and infrared scanner (VIRS) [
The real-time TRMM multisatellite precipitation analysis [
The input to VAR consists of the TRMM real-time HQ merged passive microwave precipitation estimates and the NOAA CPC merged global geosynchronous 11-micron infrared (geo-IR)
The algorithm
The 3-hourly merged high-quality IR estimates are summed for the calendar month, and then a large-scale bias adjustment is applied to the multisatellite estimates by use of the rain gauge data, almost exclusively over land. The monthly gauge-adjusted merged satellite estimate is then combined directly with the rain gauge estimates using inverse error variance weighting [
The GPCP (Global Precipitation Climatology Project) 1DD uses the “best” quasi-global observational estimators of underlying statistics to adjust quasi-global observational datasets that have desirable time/space coverage. Specifically, SSMI and SSMIS calculated from GPROF algorithm provide fractional occurrence of precipitation. GPCP version 2.2 SG combination provides monthly accumulation of precipitation to algorithms applied to geo-IR
The GPCP version 2.2 has been developed for its long-term (1979–present) global SG dataset to take advantage of the improved GPCC gauge analysis, which is a key input. The substantive changes relative to version 2.0 are the use of the new GPCC full data reanalysis (version 4) for 1979–2014 and the new GPCC monitoring product (version 2) thereafter and recalibration of the OLR precipitation index (OPI) data to a longer (20-year) record of the new SSM/I-era GPCP data. See Huffman et al. [
CMAP (CPC Merged Analysis of Precipitation) monthly precipitation data are available on the CPC website (
The dataset used to document the associated large-scale circulation is derived from the European Center for Medium-range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) dataset (
In addition, the hourly MTSAT-IR (Multifunctional Transport Satellite) equivalent black body temperature (Tbb) data [
Figure
Distribution of climatological JJA mean precipitation (mm month−1) of the TRMM 3B43 and GCPC V1.2 from 1998 to 2014, GPCP V2.2 and CMAP products from 1979 to 2014, and TRMM 3B40RT, 3B41RT, and 3B42RT products from 2000 to 2014. The blue dotted contours mark the topography at 500 m, 1000 m, and 1500 m above the sea level.
TRMM 3B43
GPCP V1.2
GPCP V2.2
CMAP
TRMM 3B40RT
TRMM 3B41RT
TRMM 3B42RT
In sharp contrast, the orographic rainfall is poorly represented in the CMAP products (Figure
Consistency can also be found among all TRMM and GPCP products for the climatological precipitation seasonal variation in JJA averaged in the latitudinal band between 19°N and 21°N shown in Figure
Climatological mean seasonal evolution of precipitation (mm month−1) in JJA averaged in the latitudinal band of 19°–21°N from various precipitation products as indicated in each panel.
TRMM 3B43
GPCP V1.2
GPCP V2.2
CMAP
TRMM 3B40RT
TRMM 3B41RT
TRMM 3B42RT
Precipitation in the Asian monsoon region displays large interannual variability. In order to compare the ability of different products in depicting the interannual variation of precipitation in this region, two cases, that is, JJA of 2002 and 2005, are chosen. As shown in Figure
The precipitation amount (mm month−1) over 92°-93°E, 19°–21°N in JJA of 2002 and 2005.
Precipitation products | 2002 JJA | 2005 JJA |
---|---|---|
TRMM 3B43 | 907.6 | 899.9 |
GPCP V1.2 | 579.7 | 561.7 |
GPCP V2.2 | 537.4 | 516.8 |
TRMM 3B40RT | 499.1 | 551.2 |
TRMM 3B41RT | 463.9 | 435.3 |
TRMM 3B42RT | 777.5 | 853.7 |
JJA mean precipitation (mm month−1) distribution in 2002 ((a)–(f)) and 2005 ((g)–(l)). The blue dotted contours mark the topography at 500 m, 1000 m, and 1500 m above sea level.
3B43 2002
GPCP V1 2002
GPCP V2 2002
3B40RT 2002
3B41RT 2002
3B42RT 2002
3B43 2005
GPCP V1 2005
GPCP V2 2005
3B40RT 2005
3B41RT 2005
3B42RT 2005
JJA mean precipitation (mm month−1) averaged within latitudinal band of 19–21°N in 2002 and 2005.
3B43
GPCP V1
GPCP V2
3B40RT
3B41RT
3B42RT
TRMM 3B40RT product agrees well with TRMM 3B43 in the rain maximum distribution. The rainfall center shifts westward to the open ocean in JJA 2005 relative to that in 2002 (Figures
However, the GPCP V2.2 shows the precipitation maximum at the same position in 2002 and 2005 (Figures
Both the spatial rainfall distribution and rainfall intensity (Figures
Summary of the precipitation products in representing the differences in the offshore rainfall maximum and intensity in the east BoB between 2002 JJA and 2005 JJA.
Precipitation products | More offshore located rainfall maximum in 2005 | Greater rainfall intensity in 2005 near shore |
---|---|---|
TRMM 3B43 | Yes | No |
GPCP V1.2 | Yes | No |
GPCP V2.2 | No | No |
TRMM 3B40RT | Yes | Yes |
TRMM 3B41RT | Yes | No |
TRMM 3B42RT | No | Yes |
A question rises as to which precipitation product is more reliable over the east BoB in the summer monsoon season in terms of the offshore precipitation maximum feature. To answer this question, we analyzed the satellite Tbb data for a comparison. Figure
JJA mean black body temperature (Tbb in °C) distribution in 2002 (a), 2005 (b), and their difference (c). The purple dotted contours are the topography at 500 m, 1000 m, and 1500 m above sea level.
2002JJA
2005JJA
2005–2002
Furthermore, previous modeling studies based on short (~1 day) integrations indicate that the observed displacement of the rainfall maximum away from the mountain summit is sensitive to the vertical shear of the prevailing winds and latent and sensible heat fluxes from the ocean [
Figure
JJA mean zonal wind (m s−1) and vertical motion (−1.0 × 10−2 Pa s−1) from the ECMWF ERA-Interim data at 0.75° resolution in 2002 ((a), (d)), 2005 ((b), (e)), and their difference ((c), (f)) averaged within latitudinal band of 19–21°N.
U 2002JJA
U 2005JJA
U 2005–2002
W 2002JJA
W 2005JJA
W 2005–2002
JJA mean sea surface temperature (SST in °C) derived from OISST data in 2002 (a), 2005 (b), and their difference (c).
2002JJA
2005JJA
2005–2002
In this study, we have compared several commonly used precipitation products to examine their strength in representing the precipitation features during the fully developed Asian summer monsoon season in the east BoB. The outstanding features of the monsoon rainfall in this area lie not only in its large precipitation amount, but also in its geographical distribution. The rainfall maximum is northwest-southeast-oriented, along the eastern coast of the BoB and on the windward side of the mountain summit. In climatology, all precipitation products present similar distribution of rainfall distribution except for the CMAP. However, significant discrepancies are found among different precipitation products in the year-to-year variation in both precipitation distribution and intensity, as illustrated by contrasting the summer rainfall in 2002 and 2005. Based on the TRMM products (except for 3B42RT) and GPCP V1.2, the precipitation maximum was located near the coast in JJA of 2002 while being about 100–200 km offshore over the open sea in 2005. However, the rainfall distribution derived from GPCP V2.2 product and TRMM 3B42RT shows little difference between 2002 and 2005 summers, with near shore rainfall maximum. For the rainfall intensity, only TRMM 3B40RT and 3B42RT show larger rainfall amount in both the oceanic and coastal regions in JJA 2005 than that in JJA 2002.
The distribution of Tbb also suggests more active convection in 2005 not only in open ocean but also near shore than in 2002 (Figure
Comparing the algorithms of these precipitation products, only the TRMM 3B40RT is primarily based on the passive microwave, while others are from IR estimation (TRMM 3B41RT) or the combination of passive microwave and IR estimations (TRMM 3B42RT, TRMM 3B43, and GPCP products). The discrepancies of these precipitation products in representing the offshore precipitation features in the east BoB may imply that the passive microwave-based precipitation is more reliable in the east BoB than the IR estimated rainfall.
Note that the interannual variability of the offshore rainfall distribution in the eastern BoB is inferred from the contrasts between two summer monsoon seasons (2002 and 2005). Although a complete comparison through the whole analysis period (1998–2014) could be done, we found that 17 years are still too short for a statistical analysis for the interannual variability. Nevertheless, the two typical years we chose provide the insight into the physical processes behind the difference in the rainfall distribution in the two years.
In addition, the widely used CMAP data fails to detect the orographically forced rainfall along the eastern coast of the BoB. The rainfall center in the CMAP appears over the peak of the Araka Yoma both in the climatology and in any particular year. Its failure could not be explained by its coarse resolution since GPCP V2.2 with the same resolution of 2.5° × 2.5° gives comparable results as those from the higher resolution TRMM precipitation products. This indicates that the CMAP data have lower skill in capturing the orographically forced rainfall distribution associated with the mesoscale mountains in the studied region. Therefore, special attention should be given to the reliability and quality of the various precipitation datasets when regional and mesoscale features of precipitation in regions with orographic effects are a major concern.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This study has been supported by the National Key Technology R&D Program of China with Grant no. 2012BAC22B03, the PICSC/USGS Grant G12AC20501 awarded to the University of Hawaii at Manoa, a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), and Qing Lan Project. The data for this paper are available at