TRMM 3B42 Research Articles

Rainfall From Resolved Rather Than Parameterized Processes Better Represents the Present-Day and Climate Change Response of Moderate Rates in the Community Atmosphere Model.

Deficiencies in the parameterizations of convection used in global climate models often lead to a distorted representation of the simulated rainfall intensity distribution (i.e., too much rainfall from weak rain rates). While encouraging improvements in high percentile rainfall intensity have been found as the horizontal resolution of the Community Atmosphere Model is increased to ∼25 km, we demonstrate no corresponding improvement in the moderate rain rates that generate the majority of accumulated rainfall. Using a statistical framework designed to emphasize links between precipitation intensity and accumulated rainfall beyond just the frequency distribution, we show that CAM cannot realistically simulate moderate rain rates, and cannot capture their intensification with climate change, even as resolution is increased. However, by separating the parameterized convective and large‐scale resolved contributions to total rainfall, we find that the intensity, geographic pattern, and climate change response of CAM's large‐scale rain rates are more consistent with observations (TRMM 3B42), superparameterization, and theoretical expectations, despite issues with parameterized convection. Increasing CAM's horizontal resolution does improve the representation of total rainfall intensity, but not due to changes in the intensity of large‐scale rain rates, which are surprisingly insensitive to horizontal resolution. Rather, improvements occur through an increase in the relative contribution of the large‐scale component to the total amount of accumulated rainfall. Analysis of sensitivities to convective timescale and entrainment rate confirm the importance of these parameters in the possible development of scale‐aware parameterizations, but also reveal unrecognized trade‐offs from the entanglement of precipitation frequency and total amount.

Evaluation and hydrological application of satellite-based precipitation datasets in driving hydrological models over the Huifa river basin in Northeast China

Satellite-based precipitation estimates with high spatial and temporal resolution and large areal coverage have provided hydrologists a potential alternative source for hydrological applications since the last few years, especially for ungauged regions. This study evaluates five satellite-based precipitation datasets, namely, Fengyun, TRMM 3B42, TRMM 3B42RT, CMORPH_BLD and CMORPH_RAW, against gauge observations for streamflow simulation with a distributed hydrological model (SWAT) over the Huifa river basin, Northeast China. Results show that, by comparing the statistical indices (MA, M5P, STDE, ME, BIAS and CC) and inter-annual precipitation, it is demonstrated that Fengyun TRMM 3B42 and CMORPH_BLD show better agreement with gauge precipitation data than CMORPH_RAW and TRMM 3B42RT. When the SWAT model for each dataset calibrated and validated individually, satisfactory model performances (defined as: NS > 0.5) are achieved at daily scale for Fengyun, TRMM 3B42 and gauge-driven model, and very good performances (defined as: NS > 0.75) are achieved at monthly scale for Fengyun and gauge-driven model, respectively. The CMORPH_BLD forced daily simulations also yield higher values of NS and R2 than CMORPH_RAW and TRMM 3B42RT at daily and monthly step. From the uncertainty results, variations of P-factor values and frequency distribution curves of NS suggest that the simulation uncertainty increase when operating the Fengyun, 3B42RT, CMORPH_BLD and CMORPH_RAW-driven model with best fitted parameters for rain gauge SWAT model. The results also indicate that the influence of parameter uncertainty on model simulation results may be greater than the effect of input data accuracy. It is noted that uncertainty analysis is necessary to evaluate the hydrological applications of satellite-based precipitation datasets.

Evaluation of precipitation trends from high-resolution satellite precipitation products over Mainland China

Many studies have reported the excellent ability of high-resolution satellite precipitation products (0.25° or finer) to capture the spatial distribution of precipitation. However, it is not known whether the precipitation trends derived from them are reliable. For the first time, we have evaluated the annual and seasonal precipitation trends from two typical sources of high-resolution satellite-gauge products, TRMM 3B43 and PERSIANN-CDR, using rain gauge observations over China, and they were also compared with those from gauge-only products (0.25° and 0.5° precipitation products, hereafter called CN25 and CN50). The evaluation focused mainly on the magnitude, significance, sign, and relative order of the precipitation trends, and was conducted at gridded and regional scales. The following results were obtained: (1) at the gridded scale, neither satellite-gauge products precisely measure the magnitude of precipitation trends but they do reproduce their sign and relative order; regarding capturing the significance of trends, they exhibit relatively acceptable performance only over regions with a sufficient amount of significant precipitation trends; (2) at the regional scale, both satellite-gauge products generally provide reliable precipitation trends, although they do not reproduce the magnitude of trends in winter precipitation; and (3) overall, CN50 and TRMM 3B43 outperform others in reproducing all four aspects of the precipitation trends. Compared with CN25, PERSIANN-CDR performs better in determining the magnitude of precipitation trends but marginally worse in reproducing their sign and relative order; moreover, both of them are at a level in capturing the significance of precipitation trends.

Spatial-temporal dynamics of rainstorm in East China based on spatial-temporal data mining technology

Based on TRMM 3B42 precipitation data, 5 time series at annual scale were constructed. Then the spatial and temporal dynamics of rainstorm in East China region from 2003 to 2015 were evaluated in the support of data mining and GIS spatial analysis technology. The results showed that, the spatial distributions of annual precipitation and annual rainstorm precipitation were similar during 2003-2015. Annual precipitation exhibited a slight increase trend while annual rainstorm ratio showed significant fluctuation. The imbalanced distribution of rainstorm in latitudinal direction was more obvious than that of longitudinal direction. Annual rainstorm frequency changed smoothly with significant spatial difference. Rainstorm showed a tendency of moving to north in the past 13 years. The changing trends of the five variables showed significant spatial differences, and most pixels showed non-significant increasing (or decreasing) trends.

Diurnal Variations of Rainfall in Surface and Satellite Observations at the Monsoon Coast (South China)

The complex features of rainfall diurnal cycles at the south China coast are examined using hourly rain gauge data and satellite products (CMORPH and TRMM 3B42) during 1998–2014. It is shown that morning rainfall is pronounced near the coasts and windward mountains, with high rainfall in the summer monsoon season, while afternoon rainfall is dominant on land, and nocturnal rainfall occurs at northern inland sites. Both satellite products report less morning rainfall and more afternoon rainfall than the rain gauge data, and they also miss the midnight rainfall minimum. These errors are mainly attributable to an underestimation of morning moderate and intense rains at coasts and an overestimation of afternoon–evening light rains on land. With a correction of the systematic bias, satellite products faithfully resolve the spatial patterns of normalized rainfall diurnal cycles related to land–sea contrast and terrains, suggesting an improved data application for regional climate studies. In particular, they are comparable to the rain gauge data in showing the linear reduction of morning rainfall from coasts to inland regions. TRMM is marginally better than CMORPH in revealing the overall features of diurnal cycles, while higher-resolution CMORPH captures more local details. All three datasets also present that morning rainfall decreases from May–June to July–August, especially on land; it exhibits pronounced interannual variations and a decadal increase in 1998–2008 at coasts. Such long-term variations of morning rainfall are induced by the coastal convergence and mountain liftings of monsoon shear flow interacting with land breeze, which is mainly regulated by monsoon southwesterly winds in the northern part of the South China Sea.

Intra‐seasonal rainfall variability in the Amazon basin related to large‐scale circulation patterns: a focus on western Amazon–Andes transition region

ABSTRACTThis study aims to relate the intra‐seasonal rainfall variability over the Amazon basin to atmospheric circulation patterns (CPs), with particular attention to extreme rainfall events in the Amazon–Andes region. The CPs summarize the intra‐seasonal variability of atmospheric circulation and are defined using daily low‐level winds from the ERA‐Interim (1.5° × 1.5°) reanalysis for the 1979–2014 period. Furthermore, observational data of precipitation and high‐resolution TRMM 3B42 (∼25 km), 2A25 PR (∼5 km) and CHIRPS (∼5 km) data products are related to the CPs throughout the Amazon basin. Nine CPs are determined using a hybrid method that combines a neural network technique (self‐organizing maps, SOM) and hierarchical ascendant classification. The CPs are characterized by a specific cycle with alternative transitions and a duration of 14 days on average. This configuration initially results in northerly winds to southerly winds towards the northern or eastern Amazon basin. The related rainfall suggests that it is driven mainly by CP dynamics. In addition, we demonstrate a good agreement amongst the four rainfall data sets: observed precipitation, TRMM 3B42, TRMM 2A25 PR and CHIRPS. Furthermore, special attention is given to the Amazon–Andes transition region. Over this region, two particular CPs (CP4 and CP5) are identified as the key contributors of maximum and minimum daily rainfall, respectively. Thus, during the dry season, 40.8% (11.4%) of the CP5 (CP4) days demonstrate rainfall of less than 1 mm day−1, while during the wet season, 6.2% (14.6%) of the CP5 (CP4) days show rainfall amounts higher than the seasonal 90th percentile (10.4 mm day−1). This study provides additional information concerning the intra‐seasonal circulation variability in Amazonia and demonstrates the value of using remote sensing precipitation data in this region as a tool for forecast in areas lacking observable information.

Mapping Fine Spatial Resolution Precipitation from TRMM Precipitation Datasets Using an Ensemble Learning Method and MODIS Optical Products in China

Precipitation data are important for the fields of hydrology and meteorology, and are fundamental for ecosystem monitoring and climate change research. Satellite-based precipitation products are already able to provide high temporal resolution precipitation information at a global level. However, the coarse spatial resolution has restricted their use in regional level studies. In this study, monthly fine spatial resolution land precipitation data in China was obtained by downscaling the TRMM 3B43 V7 monthly precipitation products. The downscaling model was constructed based on the ensemble learning method called random forest (RF). In addition to the RF model, the classification and regression tree (CART) model was also used to downscale the precipitation data for the purpose of comparison. The results were validated with in situ measurements. Results showed that the RF model outperformed the CART model. The downscaled precipitation data were strongly correlated with the in situ measurements. The downscaling method was applied to mapping fine spatial resolution precipitation over all of China, and is valuable for developing high spatial resolution precipitation products for studies on hydrology, meteorology, and climate science.

Evaluation of precipitation from CMORPH, GPCP-2, TRMM 3B43, GPCC, and ITPCAS with ground-based measurements in the Qinling-Daba Mountains, China.

The correspondence between five precipitation products, including CMORPH, GPCP-2, TRMM 3B43, GPCC, and ITPCAS, and ground-based measurements of precipitation were evaluated on annual, seasonal, and monthly scales during 2000–2014 in the Qinling-Daba Mountains over China, which is a significant area with vital value of climate and hydrology. Performances of the precipitation products in the relatively arid/humid years were also analyzed. In general, ITPCAS data displayed the highest accuracy, GPCP-2 and CMORPH data showed relatively poor performance, and GPCC and TRMM 3B43 data were average at different temporal scales among the five precipitation products. The Pearson correlation coefficient of each station had minor fluctuations for the five precipitation products. A larger deviation was found at Wudu station, most likely due to the undulating terrain. The performances of the precipitation products from highest to least accuracy are as follows: ITPCAS > TRMM 3B43 > GPCC > GPCP-2 > CMORPH. Except for CMORPH (-20.76%), the percentage precipitation differences (PPDs) of the other four precipitation products fluctuated in the range of 10% during the relatively arid (2001) and humid (2011) years. In addition, all precipitation products and ground gauge observed precipitation did not show an obvious gradient with altitude, which is different from that in other mountainous areas and is perhaps due to complex terrain, lack of observation in high altitudes, and precipitation undercatch. In consideration of the significance of Qinling-Daba Mountains as the geographic and ecological dividing lines, the present study may provide a new perspective for hydrological, climatic, and ecological researches and practices in local and other mountainous areas.

Application of a Hybrid Interpolation Method Based on Support Vector Machine in the Precipitation Spatial Interpolation of Basins

In this paper, we applied the support vector machine (SVM) to the spatial interpolation of the multi-year average annual precipitation in the Three Gorges Region basin. By combining it with the inverse distance weighting and ordinary kriging method, we constructed the SVM residual inverse distance weighting, as well as the SVM residual kriging precipitation interpolation model and compared them with the inverse distance weighting, ordinary kriging, linear regression residual inverse distance weighting and linear regression residual kriging interpolation methods. The TRMM 3B43 V7 satellite precipitation information, which is processed by the latest revision algorithm, is used as the auxiliary variable for ground site precipitation interpolation along with latitude and elevation. Our results show that: (1) adding the TRMM 3B43 V7 satellite precipitation data as an auxiliary variable significantly improves the interpolation accuracy of the linear regression equation and SVM model; (2) the support vector machine hybrid interpolation method obtains superior interpolation results compared to the inverse distance weighting method, ordinary kriging method and linear regression hybrid interpolation method; (3) the interpolation accuracy of the SVM hybrid interpolation method depends on the SVM fitting degree, so we should choose a suitable fitting accuracy rather than the highest fitting accuracy; (4) the linear regression equation has a greater degree of dependency on the TRMM data than the SVM. The SVM accepts the TRMM data information while better maintaining its independence, taking into account that the TRMM data linear regression and linear regression hybrid interpolation method are not suitable for TRMM data evaluation.

Association between cloudiness and rainfall over Fars province in Iran

Relationship between precipitation sum and cloud properties over Fars province in Iran was analyzed for the cases of light (4 mm), moderate (17 mm), and heavy (62 mm) precipitation. The cloud properties (temperature and pressure at the top, cloud optical thickness and cloud water path) were obtained from satellite data of spectoradiometer MODIS (MODO6). The spatial distribution of rainfall was obtained from the 3-hourly data of TRMM (3B42). The multivariate regression model was developed to predict the spatial distribution of rainfall. A strong significant positive association between the spatial distribution of cloud characteristics and heavy precipitation was found, while no clear correlation was revealed between light precipitation and cloud properties. The developed regression model comprised 64, 47, and 24% of spatial variance of heavy, moderate, and light rainfall, respectively. The influence of cloud water path on the spatial distribution of rainfall dominates.

Assessing Terrestrial Water Storage and Flood Potential Using GRACE Data in the Yangtze River Basin, China

Floods have caused tremendous economic, societal and ecological losses in the Yangtze River Basin (YRB) of China. To reduce the impact of these disasters, it is important to understand the variables affecting the hydrological state of the basin. In this study, we used Gravity Recovery and Climate Experiment (GRACE) satellite data, flood potential index (FPI), precipitation data (Tropical Rainfall Measuring Mission, TRMM 3B43), and other meteorological data to generate monthly terrestrial water storage anomalies (TWSA) and to evaluate flood potential in the YRB. The results indicate that the basin contained increasing amounts of water from 2003 to 2014, with a slight increase of 3.04 mm/year in the TWSA. The TWSA and TRMM data exhibit marked seasonal characteristics with summer peaks and winter dips. Estimates of terrestrial water storage based on GRACE, measured as FPI, are critical for understanding and predicting flooding. The 2010 flood (FPI ~ 0.36) was identified as the most serious disaster during the study period, with discharge and precipitation values 37.95% and 19.44% higher, respectively, than multi-year average values for the same period. FPI can assess reliably hydrological extremes with high spatial and temporal resolution, but currently, it is not suitable for smaller and/or short-term flood events. Thus, we conclude that GRACE data can be effectively used for monitoring and examining large floods in the YRB and elsewhere, thus improving the current knowledge and presenting potentially important political and economic implications.

Effects of 4D-Var Data Assimilation Using Remote Sensing Precipitation Products in a WRF Model over the Complex Terrain of an Arid Region River Basin

Individually, ground-based, in situ observations, remote sensing, and regional climate modeling cannot provide the high-quality precipitation data required for hydrological prediction, especially over complex terrains. Data assimilation techniques can be used to bridge the gap between observations and models by assimilating ground observations and remote sensing products into models to improve precipitation simulation and forecasting. However, only a small portion of satellite-retrieved precipitation products assimilation research has been implemented over complex terrains in an arid region. Here, we used the weather research and forecasting (WRF) model to assimilate two satellite precipitation products (The Tropical Rainfall Measuring Mission: TRMM 3B42 and Fengyun-2D: FY-2D) using the 4D-Var data assimilation method for a typical inland river basin in northwest China’s arid region, the Heihe River Basin, where terrains are very complex. The results show that the assimilation of remote sensing precipitation products can improve the initial WRF fields of humidity and temperature, thereby improving precipitation forecasting and decreasing the spin-up time. Hence, assimilating TRMM and FY-2D remote sensing precipitation products using WRF 4D-Var can be viewed as a positive step toward improving the accuracy and lead time of numerical weather prediction models, particularly over regions with complex terrains.

Simulation of the diurnal variation of rainfall over the western Maritime Continent using a regional climate model

This study evaluates the performance of the MIT regional climate model (MRCM) in simulating the temporal and spatial structure of the diurnal variation of rainfall over the western Maritime Continent. In order to investigate the effect of model resolution, two identical simulations with 27 and 12 km horizontal resolutions are performed for a 30-year period (1982–2011). The simulated climatological features are compared with the TRMM 3B42 3-h observations. The analysis is focused on the regional characteristics of diurnal variation of rainfall in terms of phase and amplitude, with an emphasis on the difference in behaviors between land and ocean. Systematic modulation of the diurnal cycle over land and ocean characterizes the rainfall pattern over the Maritime Continent. The evening peak with strong amplitude over land and the morning peak with weak amplitude over ocean reflect the contrast in behavior between land and ocean. In general, both simulations are able to capture major features of diurnal rainfall variations with similarity in several aspects to TRMM observation. However, the improvement from increasing resolution is more apparent in the coastal and offshore areas, where rainfall processes are strongly tied with low-level wind that varies diurnally and regionally. A more realistic coastline and a sharp gradient of elevation derived from high resolution boundary conditions enhance the local circulation associated with land-sea breeze and topographic complexity, which in turn induces a favorable condition for the offshore convergence and associated rainfall occurrence. The MRCM with 12 km resolution simulates propagation of rainfall from inland to coastal or offshore areas, such as in the vicinity of western Sumatra, northern Java, and western Borneo Islands. However, further improvements can be gained from even higher resolution models, such as convection-permitting scale.

Tracking precipitation events in time and space in gridded observational data

AbstractNovel precipitation event data sets are created by tracking 3‐hourly observed rainfall in both time and space in the TRMM 3B42 and ERA‐Interim data sets. Relative to TRMM, ERA‐Interim data undersimulate the total number of events (factor of ∼0.5), and oversimulate the frequency of events lasting >5 days (factor of 1.6). Longer‐lasting events tend to have larger spatial footprints and higher intensity precipitation at any point in their lifetime, and thus contribute significantly more to total precipitation than shorter events. Precipitation changes in selected tropical and subtropical regions are attributed to different event characteristics: some to changes in event rainfall intensity, others to changes in the number of events. In the 40°S–40°N spatial average, the number of events lasting 1–5 days significantly increased from 1998 to 2014 in both the TRMM 3B42 and ERA‐Interim data. The event data sets, analysis scripts, and selected processed data are freely available online.

Evaluation of CMIP5 Model Precipitation Using PERSIANN-CDR

Abstract The purpose of this study is to use the PERSIANN–Climate Data Record (PERSIANN-CDR) dataset to evaluate the ability of 32 CMIP5 models in capturing the behavior of daily extreme precipitation estimates globally. The daily long-term historical global PERSIANN-CDR allows for a global investigation of eight precipitation indices that is unattainable with other datasets. Quantitative comparisons against CPC daily gauge; GPCP One-Degree Daily (GPCP1DD); and TRMM 3B42, version 7 (3B42V7), datasets show the credibility of PERSIANN-CDR to be used as the reference data for global evaluation of CMIP5 models. This work uniquely defines different study regions by partitioning global land areas into 25 groups based on continent and climate zone type. Results show that model performance in warm temperate and equatorial regions in capturing daily extreme precipitation behavior is largely mixed in terms of index RMSE and correlation, suggesting that these regions may benefit from weighted model averaging schemes or model selection as opposed to simple model averaging. The three driest climate regions (snow, polar, and arid) exhibit high correlations and low RMSE values when compared against PERSIANN-CDR estimates, with the exceptions of the cold regions showing an inability to capture the 95th and 99th percentile annual total precipitation characteristics. A comprehensive assessment of each model’s performance in each continent–climate zone defined group is provided as a guide for both model developers to target regions and processes that are not yet fully captured in certain climate types, and for climate model output users to be able to select the models and/or the study areas that may best fit their applications of interest.

Validation of TRMM 3B42 V6 for estimation of mean annual rainfall over ungauged area in semiarid climate

This research compares data from the Tropical Rainfall Measuring Mission 3B42 V6 with data obtained from 19 synoptic rain gauges during the period 1998–2010 over the semiarid climate of Khorasan Razavi, Iran. Validation was performed using three spatial extents, including 1 TRMM grid face from the synoptic station (1PTRM), 3 TRMM points surrounding the synoptic station (3PTRM) and 5 TRMM points surrounding the synoptic station (5PTRM), using ArcGIS 10.2 software. The perfect and poor r were obtained at stations S08 and S19, with values of 0.92 and 0.26, respectively. According to the Nash–Sutcliffe efficiency coefficient, the TRMM satellite can predict the spatial variation of the mean annual rainfall by 0.23, 0.43 and 0.38 for 1PTRM, 3PTRM and 5PTRM, respectively, at 19 stations. The agreement significantly increases by 0.88, 0.83 and 0.80 for 1PTRM, 3PTRM and 5PTRM, respectively, when gauges S05, S07, S11 and S13 are excluded from the dataset, which may be associated with orographic or instrumental error at the stations.

Temporal and Spatial Analysis of Precipitation in Guizhou Based on TRMM 3B42 Satellite Data

Precipitation is an important part of the earth’s climate system. This article makes full use of the advantages of remote sensing images. In this paper, the TRMM 3B42 satellite precipitation from 1998 to 2013 is selected as the data source and Guizhou Province as the research area. The temporal and spatial distribution characteristics of precipitation in Guizhou Province are studied by linear trend estimation, linear regression analysis and ArcGIS spatial analysis. The conclusion as below: Precipitation shows a decreasing trend from southeast to northwest in Guizhou Province. From the seasonal scale, Precipitation is mainly concentrated in summer and the least precipitation in winter. Over the past 16 years, precipitation in Guizhou Province has shown a fluctuating change. Summer precipitation trend is more obvious. And winter precipitation is not obvious.

Dependence of estimated precipitation frequency and intensity on data resolution

Precipitation frequency (F) and intensity (I) are important characteristics that climate models often fail to simulate realistically. Their estimates are highly sensitive to the spatial and temporal resolutions of the input data and this further complicates the comparison between models and observations. Here, we analyze 3-hourly precipitation data on a 0.25° grid from two satellite-derived datasets, namely TRMM 3B42 and CMORPH_V1.0, to quantify this dependence of the estimated precipitation F and I on data resolution. We then develop a simple probability-based relationship to explain this dependence, and examine the spatial and seasonal variations in the estimated F and I fields. As expected, precipitation F (I) increases (decreases) with the size of the grid box or time interval over which the data are averaged, but the magnitude of this change varies with location, and is strongest in the tropics and weakest in the subtropics. Our simple relationship can quantitatively explain this dependence of the estimated F and I on the spatial or temporal resolution of the input data. This demonstrates that large differences in the estimated F and I can arise purely from the differences in the spatial or temporal resolution of the input data. The results highlight the need to have similar resolution in comparing two datasets or between observations and models. Our estimates show that extremely low frequencies (<1%) are seen over the subtropics while the highest frequencies (20–40%) are located mostly over the tropics, and that the high frequency results from both longer and more frequent precipitation events. Precipitation intensity is more uniformly distributed than frequency. Strong correlations between the amount and frequency confirm the notion that the frequency plays a bigger role than intensity in determining precipitation variations.

Two-Step Downscaling of Trmm 3b43 V7 Precipitation in Contrasting Climatic Regions With Sparse Monitoring: The Case of Ecuador in Tropical South America

Spatial prediction of precipitation with high resolution is a challenging task in regions with strong climate variability and scarce monitoring. For this purpose, the quasi-continuous supply of information from satellite imagery is commonly used to complement in situ data. However, satellite images of precipitation are available at coarse resolutions, and require adequate methods for spatial downscaling and calibration. The objective of this paper is to introduce and evaluate a 2-step spatial downscaling approach for monthly precipitation applied to TRMM 3B43 (from 0 . 25 ∘ ≈ 27 km to 5 km resolution), resulting in 5 downscaled products for the period 01-2001/12-2011. The methodology was evaluated in 3 contrasting climatic regions of Ecuador. In step 1, bilinear resampling was applied over TRMM, and used as a reference product. The second step introduces further variability, and consists of four alternative gauge-satellite merging methods: (1) regression with in situ stations, (2) regression kriging with in situ stations, (3) regression with in situ stations and auxiliary variables, and (4) regression kriging with in situ stations and auxiliary variables. The first 2 methods only use the resampled TRMM data set as an independent variable. The last 2 methods enrich these models with auxiliary environmental factors, incorporating atmospheric and land variables. The results showed that no product outperforms the others in every region. In general, the methods with residual kriging correction outperformed the regression models. Regression kriging with situ data provided the best representation in the Coast, while regression kriging with in situ and auxiliary data generated the best results in the Andes. In the Amazon, no product outperformed the resampled TRMM images, probably due to the low density of in situ stations. These results are relevant to enhance satellite precipitation, depending on the availability of in situ data, auxiliary satellite variables and the particularities of the climatic regions.

Major role of water bodies on diurnal precipitation regimes in Eastern Africa

ABSTRACTMean diurnal rainfall regimes over Eastern Africa (also referred to as the Greater Horn of Africa) are studied based on 3‐hourly data from the TRMM 3B42 data set, averaged over a 17‐year period (1998–2014). The consistency with long‐term mean raingauge data, available for partly independent periods, varies from good (Sudan, Ethiopia, Eritrea and Somalia) to very good (Kenya, Tanzania and Uganda). Over sea (Indian Ocean and Red Sea), the diurnal rainfall distribution is quite uniform; however, a morning peak dominates and there is evidence of offshore phase propagation south of the equator. Over land, both rainfall frequency and rainfall amounts show a dominant afternoon maximum (1500–1800 East African Time, i.e. GMT + 3). However, many inland regions show a delayed rainfall maximum (evening, night‐time or morning). The evening to night‐time maximum found over some land areas is associated with a phase propagation from areas showing an afternoon peak. This occurs west of high ground areas (Sudan and parts of the Great Lakes region) and in belts parallel to the seashores (Eritrea, northeastern Ethiopia, Somalia and eastern Kenya). The latter provide indirect evidence that sea breeze effects can be detected at unexpectedly great distances from the coast (up to 300–400 km) in parts of Eastern Africa. A remarkable ring of early afternoon (1500) maxima is found around most lakes, although some east–west asymmetries occur. Over the lakes, a morning or late night maximum is mostly found. It is generally inversely related to the distance to the shorelines for the larger lakes, but over the mid‐size lakes it is replaced by or competes with a late afternoon to evening maximum.