Tropical Rainfall Measuring Mission Estimates Research Articles

Comparison of two bias correction methods for TRMM 3B42 satellite daily rainfall estimates over Northern Tunisia

The overall objective of this study is to evaluate and correct the Tropical Rainfall Measuring Mission (TRMM) 3B42 algorithm for Northern Tunisia focusing on heavy rainfall events. Two types of correction methods are tested. The first is the combined scheme (CoSch) which was applied in two different ways. CoSch (1) combines satellite data with interpolated in situ data. However, CoSch (2) combines satellite data with a not interpolated in situ map where the pixel value is randomly selected from the rainfall stations belonging this pixel. The second type of correction is the best linear unbiased estimator. The study period is from January 2007 to August 2009. The in situ database is composed of an average of 318 rain gauges. Heavy events are defined as those daily events exceeding 50 mm/day for at least one station. A total of 77 heavy rainfall events result from this selection criterion; 35 events were recorded during the dry period (May to October) and 42 during the wet season (November to April). We first investigate the boxplots of the various evaluation indicators for raw TRMM. The best achievement is for moderate events. The worst performance is for very light and light events. Moreover, we noticed that raw TRMM estimates perform better during wet season. The error decomposition underlined that the highest underestimated values are localized in orographic areas in Le Kef, also in Cap Bon. However, the rainfall overestimation appeared in the central part of the study area (Bizerte and Zaghouan). About the bias correction method comparison, CoSch (1) performance showed a stronger correction than the unbiased estimator which outperforms CoSch (2). As for raw TRMM, CoSch (1) reports better correlation during wet season. The correction of probability of detection (POD) is more important for the wet season reaching 0.9 by the CoSch (1) and unbiased estimator methods. The threat score (TS) coefficients are found not sensitive to the season whatever the correction method.

Comparison of TRMM multi-satellite precipitation analysis (TMPA) estimation with ground-based precipitation data over Maharashtra, India

The Tropical Rainfall Measuring Mission (TRMM) is a multi-satellite precipitation product which is important for various hydro-meteorological applications. It is important to assess the correlation between precipitation records obtained by satellite and a ground observation station. The current study focuses to understand if satellite estimates can be used over ground station. The study has used bivariate correlation analysis to assess daily and 8-day composite precipitation records of TRMM satellite and Indian Meteorological Department (IMD) over Maharashtra, India, for monsoon months during 2004–2013. The Pearson’s correlation coefficient (r) varies significantly for daily and 8-day composite analysis. It was found that the 8-day composite TRMM showed strong correlation when compared to daily rainfall amounts. From the values recorded and scrutiny carried out, it was seen that the TRMM estimates were very well in synchronization with the IMD data for western region of study area. Precipitation in this region is heavily influenced by the Western Ghats which helps in exact determination of trend. Geographical influence on rainfall is a crucial thing to be addressed while conducting such studies. Research concludes that TRMM satellite estimates when considered over weekly analysis yielded better results against IMD’s rainfall making a suitable replacement for hydro-metrological studies.

Evaluation of TRMM Precipitation Dataset over Himalayan Catchment: The Upper Ganga Basin, India

Satellite based rainfall estimation techniques have emerged as a potential alternative to ground based rainfall measurements. The Tropical Rainfall Measuring Mission (TRMM) precipitation, in particular, has been used in various climate and hydrology based studies around the world. While having wide possibilities, TRMM rainfall estimates are found to be inconsistent with the ground based rainfall measurements at various locations such as the southwest coast and Himalayan region of India, northeast parts of USA, Lake Victoria in Africa, La Plata basin in South America, etc. In this study, the applicability of TRMM estimates is evaluated over the Upper Ganga Basin (Himalayan catchment) by comparing against gauge-based India Meteorological Department (IMD) gridded precipitation records. Apart from temporal evaluation, the ability of TRMM in capturing spatial distribution is also examined using three statistical parameters namely correlation coefficient (r), mean absolute error (MAE) and relative bias (RBIAS). In the results, the dual nature of bias is evident in TRMM precipitation with rainfall magnitude falling in the range from 100 to 370 mm representing positive bias, whereas, rainfall magnitude above 400 mm, approximately, representing negative bias. The Quantile Mapping (QM) approach has been used to correct the TRMM dataset from these biases. The raw TRMM precipitation is found to be fairly correlated with IMD rainfall for post-monsoon and winter season with R2 values of 0.65 and 0.57, respectively. The R2 value of 0.41 is obtained for the monsoon season, whereas least correlation is found for the pre-monsoon season with an R2 value of 0.24. Moreover, spatial distribution of rainfall during post-monsoon and winter season is captured adequately; however, the limited efficiency of TRMM is reflected for pre-monsoon and monsoon season. Bias correction has satisfactorily enhanced the spatial distribution of rainfall obtained from TRMM for almost all the seasons except for monsoon. Overall, the corrected TRMM precipitation dataset can be used for various climate analyses and hydrological water balance based studies in the Himalayan river basins.

Hydrological Simulation Using TRMM and CHIRPS Precipitation Estimates in the Lower Lancang-Mekong River Basin

Satellite-based products with high spatial and temporal resolution provide useful precipitation information for data-sparse or ungauged large-scale watersheds. In the Lower Lancang-Mekong River Basin, rainfall stations are sparse and unevenly distributed, and the transboundary characteristic makes the collection of precipitation data more difficult, which has restricted hydrological processes simulation. In this study, daily precipitation data from four datasets (gauge observations, inverse distance weighted (IDW) data, Tropical Rainfall Measuring Mission (TRMM) estimates, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) estimates), were applied to drive the Soil and Water Assessment Tool (SWAT) model, and then their capability for hydrological simulation in the Lower Lancang-Mekong River Basin were examined. TRMM and CHIRPS data showed good performances on precipitation estimation in the Lower Lancang-Mekong River Basin, with the better performance for TRMM product. The Nash-Sutcliffe efficiency (NSE) values of gauge, IDW, TRMM, and CHIRPS simulations during the calibration period were 0.87, 0.86, 0.95, and 0.93 for monthly flow, respectively, and those for daily flow were 0.75, 0.77, 0.86, and 0.84, respectively. TRMM and CHIRPS data were superior to rain gauge and IDW data for driving the hydrological model, and TRMM data produced the best simulation performance. Satellite-based precipitation estimates could be suitable data sources when simulating hydrological processes for large data-poor or ungauged watersheds, especially in international river basins for which precipitation observations are difficult to collect. CHIRPS data provide long precipitation time series from 1981 to near present and thus could be used as an alternative precipitation input for hydrological simulation, especially for the period without TRMM data. For satellite-based precipitation products, the differences in the occurrence frequencies and amounts of precipitation with different intensities would affect simulation results of water balance components, which should be comprehensively considered in water resources estimation and planning.

Cross-validation of rainfall characteristics estimated from the TRMM PR, a combined PR-TMI algorithm and a C-POL ground-radar during the passage of tropical cyclone and non-tropical cyclone events over Darwin, Australia.

This study cross-validates the radar reflectivity Z, the rainfall drop size distribution parameter (median volume diameter, Do ) and the rainfall rate R estimated from the Tropical Rainfall Measuring Mission (TRMM) satellite Precipitation Radar (PR), a combined PR and TRMM Microwave Imager (TMI) algorithm (COM) and a C-band dual-polarised ground-radar (GR) for TRMM overpasses during the passage of tropical cyclone (TC) and non-TC events over Darwin, Australia. Two overpass events during the passage of TC Carlos and eleven non-TC overpass events are used in this study and the GR is taken as the reference. It is shown that the correspondence is dependent on the precipitation type whereby events with more (less) stratiform rainfall usually have a positive (negative) bias in the reflectivity and the rainfall rate whereas in the Do the bias is generally positive but small (large). The COM reflectivity estimates are similar to the PR but it has a smaller bias in the Do for most of the greater stratiform events. This suggests that combining the TMI with the PR adjusts the Do towards the "correct" direction if the GR is taken as the reference. Moreover, the association between the TRMM estimates and the GR for the two TC events, which are highly stratiform in nature, is similar to that observed for the highly stratiform non-TC events (there is no significant difference) but it differs largely from that observed for the majority of the highly convective non-TC events.

Does the GPM mission improve the systematic error component in satellite rainfall estimates over TRMM? An evaluation at a pan-India scale

Abstract. The last couple of decades have seen the outburst of a number of satellite-based precipitation products with Tropical Rainfall Measuring Mission (TRMM) as the most widely used for hydrologic applications. Transition of TRMM into the Global Precipitation Measurement (GPM) promises enhanced spatio-temporal resolution along with upgrades to sensors and rainfall estimation techniques. The dependence of systematic error components in rainfall estimates of the Integrated Multi-satellitE Retrievals for GPM (IMERG), and their variation with climatology and topography, was evaluated over 86 basins in India for year 2014 and compared with the corresponding (2014) and retrospective (1998–2013) TRMM estimates. IMERG outperformed TRMM for all rainfall intensities across a majority of Indian basins, with significant improvement in low rainfall estimates showing smaller negative biases in 75 out of 86 basins. Low rainfall estimates in TRMM showed a systematic dependence on basin climatology, with significant overprediction in semi-arid basins, which gradually improved in the higher rainfall basins. Medium and high rainfall estimates of TRMM exhibited a strong dependence on basin topography, with declining skill in higher elevation basins. The systematic dependence of error components on basin climatology and topography was reduced in IMERG, especially in terms of topography. Rainfall-runoff modeling using the Variable Infiltration Capacity (VIC) model over two flood-prone basins (Mahanadi and Wainganga) revealed that improvement in rainfall estimates in IMERG did not translate into improvement in runoff simulations. More studies are required over basins in different hydroclimatic zones to evaluate the hydrologic significance of IMERG.

Performance of the WRF model to simulate the seasonal and interannual variability of hydrometeorological variables in East Africa: a case study for the Tana River basin in Kenya

This study investigates the ability of the regional climate model Weather Research and Forecasting (WRF) in simulating the seasonal and interannual variability of hydrometeorological variables in the Tana River basin (TRB) in Kenya, East Africa. The impact of two different land use classifications, i.e., the Moderate Resolution Imaging Spectroradiometer (MODIS) and the US Geological Survey (USGS) at two horizontal resolutions (50 and 25 km) is investigated. Simulated precipitation and temperature for the period 2011–2014 are compared with Tropical Rainfall Measuring Mission (TRMM), Climate Research Unit (CRU), and station data. The ability of Tropical Rainfall Measuring Mission (TRMM) and Climate Research Unit (CRU) data in reproducing in situ observation in the TRB is analyzed. All considered WRF simulations capture well the annual as well as the interannual and spatial distribution of precipitation in the TRB according to station data and the TRMM estimates. Our results demonstrate that the increase of horizontal resolution from 50 to 25 km, together with the use of the MODIS land use classification, significantly improves the precipitation results. In the case of temperature, spatial patterns and seasonal cycle are well reproduced, although there is a systematic cold bias with respect to both station and CRU data. Our results contribute to the identification of suitable and regionally adapted regional climate models (RCMs) for East Africa.

Avaliação das estimativas de chuva do satélite TRMM no Estado da Paraíba / Assessment of rainfall estimates of TRMM satellite on Paraíba state

The spatio-temporal rainfall variability, the low-density of rain gauges and operational problems are complexity factors for hydrologic studies in developing countries such as Brazil. This research evaluates rainfall estimates derived from Tropical Rainfall Measuring Mission (TRMM) 3B42 product, version 7, over Paraiba state, in Northeast of Brazil. This study analyzed time series of 212 rain gauges) and TRMM data within a spatial grid of 198 pixels, for a 14-years period (1998-2011). Analyses were carried out punctually for each pluviometer as well as spatially aggregated for the mesoregions Mata, Agreste, Borborema and Sertao, considering daily, 3-days, 7-days and monthly rainfall. Largest agreement between TRMM estimates and observed rainfall was obtained for Sertao region (correlation coefficient r daily = 0,89 and absolute average error EMA monthly = 16mm) and smallest agreement for seaside region (r daily = 0,75 and EMA monthly = 31 mm), i.e., the best agreement was obtained for regions with low rainfall rates. TRMM data underestimated observed rainfall in sites with large rainfall amount (monthly bias of -24 mm in Mata region) and overestimated in sites with low rainfall (monthly bias of +13 in Sertao and of +11 mm in Borborema). The spatially detailed characterization over Paraiba of the quality of the TRMM estimates showed in this work illustrates the potential of using such rainfall estimates for this state, because knowing its errors, their types and quantification tends to raise the trust in using them and also allows applying correction methods.

Rainfall in an experimental watershed: a comparison between observed and TRMM 3B42V7 dataset

Abstract. In 2003, a network for hydrology of the semiarid region (REHISA in Portuguese) was created in Brazil. Since then, experimental watersheds in this region has been providing hydrometeorological data collected in automatic stations. However, the spatial distribution of these gauges might be insufficient to thoroughly understand the hydrological processes occurring in the area. Remotely sensed hydrological variables presents a possible way to overcome such limitations as long as these estimates prove to have enough accuracy. This paper compares the monitored yearly and monthly rainfall in the Guaraíra experimental watershed with data from the Tropical Rainfall Measuring Mission (TRMM) 3B42 Version-7 product. The study area has a drainage area of 5.84 km² and is located in the coastal region of Paraíba State, where the mean annual rainfall is 1,700 mm. Two automatic stations provided rainfall data from 2004 to 2011 to assess the satellite estimates in annual and monthly basis. TRMM 3B42V7 performance was evaluated based on graphical analysis. In the annual analysis, relative error ranged from 3 to -51%, however due to the monthly variation, such errors seemed to be insufficient to draw any conclusion, regarding the monthly results. For instance, when the relative error was 3% (difference of 48.3 mm for year 2004), the monthly analysis showed that this was due to a compensation occurred during the year, this is, a month for which rainfall was significantly underestimated by TRMM was compensated by another one when the satellite rainfall was overestimated. On the opposite, in 2007 (relative error 51%, 855 mm of difference), the monthly data analysis showed that just 4 months presented observed data overestimation, but it was enough to result in such annual overestimation. The monthly analysis showed that 29% of the months presented difference between observed rainfall and TRMM data greater than 70 mm and less than 386 mm, which can be considered a relevant error. 72.4% of these cases (monthly analyses) occurred in years in which the annual rainfall were within the ordinary mean (from 1,205 to 1,760 mm/year). Another important result is the underestimation cases were concentrated on the second part of the rainy period. Thus, conclusions points out that TRMM estimates can provide useful information on annual basis, but users should be aware concerning the underestimation, specially on monthly basis for the studied region.

Precipitation climatology over the Mediterranean Basin — Validation over Cyprus

A study for a 15-year period (1998–2012) embracing both satellite and rain gauge data is performed over the Mediterranean basin. Tropical Rainfall Measuring Mission (TRMM) 3B43 monthly data (versions 7 and 7A) are compared with the corresponding E-OBS gridded rain gauge precipitation data over a common 0.25×0.25° grid. The study focuses over Cyprus, where a dense and reliable network of rain gauges is available. These in situ measurements are compared with both E-OBS and TRMM data for the aforementioned 15-year period. Monthly and (seasonal) yearly comparisons are presented for each of the 0.25×0.25° cells overlaying the island of Cyprus. For each of these cells, at least three rain gauges are sited within its corresponding area. BIAS and mean absolute error (MAE) for TRMM and E-OBS estimates from the corresponding averaged gauge data were extracted. The results show a very good correlation (as high as 0.95) over areas where TRMM data incorporate rain gauge measurements along with satellite data. However, the poor correlation over other areas (as low as 0.05) is of great interest and needs to be studied further. The overall conclusion from this study is that TRMM and E-OBS estimates are well fitted with the gauge data, although they are both influenced by the variation of elevation.

Evaluation of TRMM 3B42 precipitation estimates and WRF retrospective precipitation simulation over the Pacific–Andean region of Ecuador and Peru

Abstract. The Pacific–Andean region in western South America suffers from rainfall data scarcity, as is the case for many regions in the South. An important research question is whether the latest satellite-based and numerical weather prediction (NWP) model outputs capture well the temporal and spatial patterns of rainfall over the region, and hence have the potential to compensate for the data scarcity. Based on an interpolated gauge-based rainfall data set, the performance of the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 and its predecessor V6, and the North Western South America Retrospective Simulation (OA-NOSA30) are evaluated over 21 sub-catchments in the Pacific–Andean region of Ecuador and Peru (PAEP). In general, precipitation estimates from TRMM and OA-NOSA30 capture the seasonal features of precipitation in the study area. Quantitatively, only the southern sub-catchments of Ecuador and northern Peru (3.6–6° S) are relatively well estimated by both products. The accuracy is considerably less in the northern and central basins of Ecuador (0–3.6° S). It is shown that the probability of detection (POD) is better for light precipitation (POD decreases from 0.6 for rates less than 5 mm day−1 to 0.2 for rates higher than 20 mm day−1. Compared to its predecessor, 3B42 V7 shows modest region-wide improvements in reducing biases. The improvement is specific to the coastal and open ocean sub-catchments. In view of hydrological applications, the correlation of TRMM and OA-NOSA30 estimates with observations increases with time aggregation. The correlation is higher for the monthly time aggregation in comparison with the daily, weekly, and 15-day time scales. Furthermore, it is found that TRMM performs better than OA-NOSA30 in generating the spatial distribution of mean annual precipitation.

Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi)

Abstract. The paper examines the quality of satellite-based precipitation estimates for the lower Mahanadi River basin (eastern India). The considered data sets known as 3B42 and 3B42-RT (version 7/7A) are routinely produced by the tropical rainfall measuring mission (TRMM) from passive microwave and infrared recordings. While the 3B42-RT data are disseminated in real time, the gauge-adjusted 3B42 data set is published with a delay of some months. The quality of the two products was assessed in a two-step procedure. First, the correspondence between the remotely sensed precipitation rates and rain gauge data was evaluated at the sub-basin scale. Second, the quality of the rainfall estimates was assessed by analysing their performance in the context of rainfall–runoff simulation. At sub-basin level (4000 to 16 000 km2) the satellite-based areal precipitation estimates were found to be moderately correlated with the gauge-based counterparts (R2 of 0.64–0.74 for 3B42 and 0.59–0.72 for 3B42-RT). Significant discrepancies between TRMM data and ground observations were identified at high-intensity levels. The rainfall depth derived from rain gauge data is often not reflected by the TRMM estimates (hit rate < 0.6 for ground-based intensities > 80 mm day-1). At the same time, the remotely sensed rainfall rates frequently exceed the gauge-based equivalents (false alarm ratios of 0.2–0.6). In addition, the real-time product 3B42-RT was found to suffer from a spatially consistent negative bias. Since the regionalisation of rain gauge data is potentially associated with a number of errors, the above results are subject to uncertainty. Hence, a validation against independent information, such as stream flow, was essential. In this case study, the outcome of rainfall–runoff simulation experiments was consistent with the above-mentioned findings. The best fit between observed and simulated stream flow was obtained if rain gauge data were used as model input (Nash–Sutcliffe index of 0.76–0.88 at gauges not affected by reservoir operation). This compares to the values of 0.71–0.78 for the gauge-adjusted TRMM 3B42 data and 0.65–0.77 for the 3B42-RT real-time data. Whether the 3B42-RT data are useful in the context of operational runoff prediction in spite of the identified problems remains a question for further research.

Application of Remotely Sensed Rainfall Data in Rainfall-Runoff Modelling. A Case of Pangani River Basin, Tanzania

Rainfall runoff modelling in a river basin is vital for number of hydrologic applicationincluding water resources assessment. However, rainfall data from sparse gauging stationsare usually inadequate for modelling which is a major concern in Tanzania. This studypresents the results of comparison of Tropical Rainfall Measuring Mission (TRMM)satellite rainfall products at daily and monthly time-steps with ground stations rainfalldata; and explores the possibility of using satellite rainfall data for rainfall runoffmodelling in Pangani River Basin, Tanzania. Statistical analysis was carried out to find thecorrelation between the ground stations data and TRMM estimates. It was found thatTRMM estimates at monthly scale compare reasonably well with ground stations data.Time series comparison was also done at daily and annual time scales. Monthly and annualtime series compared well with coefficient of determination of 0.68 and 0.70, respectively.It was also found that areal rainfall comparison in the northern parts of the study area hadpoor results compared to the rest of areas. On the other hand, rainfall runoff modellingwith ground stations data alone and TRMM data set alone was carried out using five Real-Time River Flow Forecasting System models and then outputs combined by Models OutputsCombination Techniques. The results showed that ground stations data performed betterduring calibration period with coefficient of efficiency of 76.7%, 81.7% and 89.1% forSimple Average Method, Weight Average Method and Neural Network Method respectively.Simulation results using TRMM data were 59.8%, 73.5% and 76.8%. It can therefore beconcluded that TRMM data are adequate and promising in hydrological modelling.

Spatial and temporal variability of daily monsoon rainfall in Tunga and Bhadra River basins, Karnataka

The Tropical Rainfall Measuring Mission (TRMM) rainfall product 3B42-V7 and the telemetric station data sets of years 2010 and 2011 for the monsoon season were used to study the spatial and temporal variability of the daily rainfall in Tunga and Bhadra River basins in Karnataka. The daily rain observations of gauge and TRMM estimates show correlation coefficient values of 0.63 for the stations located within the Western Ghats (highland) and 0.40 for the stations located away from the Western Ghats (midland). The threshold of 50% and 75% of the mean surface rainfall gives a good accuracy of 0.89 and 0.4 for midland stations and 0.62 for highland stations. The reliability of the TRMM data varies depending on the root mean square error of 10.74 mm in highland region and 5.25 mm in midland region. The amplitude of the variability is high over the highland stations; this may be due to the orography and the inconsistency in cell resolution of the satellite to that of the point data of the rain gauging stations. The variability in the diurnal time scale is also studied and it is found that high rainfall activity is in the afternoon hours in both the highland and midland regions. A bimodal variation during the active monsoon phase and unimodal variation during rest of the season is noticed in the highland region of the area, and unimodal variation in midland region is noticed throughout the south-west monsoon period.

Comparison of TRMM Data with Rain Gauge Observations in the Upper Huaihe River Basin of China

In this study, the Tropical Rainfall Measuring Mission (TRMM) version 7 satellite rainfall product, TRMM 3B42 (V7), was validated using rain gauge measurements in the Upper Huaihe Basin, China. This validation was carried out at monthly and annual temporal scales for an 11-year period using four selected grids with six, four, two and one rain gauge station (s) located within the TRMM grid respectively; the rain gage measurements for grids with more than one rain gauge were averaged. This study found that the validation of the TRMM dataset in grids where there were adequate rain gauge were present to capture the distributed and stochastic nature of rainfall with very good correlation (0.87-0.94) and with very little relative bias when the rain gage accumulations were compared with the TRMM estimates. From the study we found that the TRMM dataset can be used as precipitation input for hydrological modeling at monthly and annual scales for sustainable water resources management in the Upper Huaihe River and even in un-gaged or sparsely gaged basins in other parts of the world.

Diurnal rainfall variability over the Upper Blue Nile Basin: A remote sensing based approach

In this study we aim to assess the diurnal cycle of rainfall across the Upper Blue Nile (UBN) basin using satellite observations from Tropical Rainfall Measuring Mission (TRMM). Seven years (2002–2008) of Precipitation Radar (PR) and TRMM Microwave Imager (TMI) data are used and analyses are based on GIS operations and simple statistical techniques. Observations from PR and TMI reveal that over most parts of the basin area, the rainfall occurrence and conditional mean rain rate are highest between mid- and late-afternoon (15:00–18:00 LST). Exceptions to this are the south-west and south-eastern parts of the basin area and the Lake Tana basin where midnight and early morning maxima are observed. Along the Blue Nile River gorge the rainfall occurrence and the conditional mean rain rate are highest during the night (20:00–23:00 LST). Orographic effects by large scale variation of topography, elevation and the presence of the UBN river gorge were assessed taking two transects across the basin. Along transects from north to south and from east to west results indicate increased rainfall with increase of elevation whereas areas on the windward side of the high mountain ranges receive higher amount of rainfall than areas on the leeward side. As such, mountain ranges and elevation affect the rainfall distribution resulting in rain shadow effect in the north-eastern parts of Choke-mountain and the ridges in the north-east of the basin. Moreover, a direct relation between rainfall occurrence and elevation is observed specifically for 17:00–18:00 LST. Further, results indicate that the rainfall distribution in the deeply incised and wide river gorge is affected with relatively low rainfall occurrence and low mean rainfall rates in the gorge areas. Seasonal mean rainfall depth is highest in the south-west area and central highlands of the basin while areas in the north, north-east and along the Blue Nile gorge receive the least amount of rainfall. Statistical results of this work show that the diurnal cycle of rainfall occurrence from TRMM estimates show significant correlation with the ground observations at 95% confidence level. In the UBN basin, the PR conditional mean rain rate estimates are closer to the ground observations than the TMI. Analysis on mean wet season rainfall amount indicates that PR generally underestimates and TMI overestimates the ground observed rainfall.

Vertical Diabatic Heating Structure of the MJO: Intercomparison between Recent Reanalyses and TRMM Estimates

Abstract Capitalizing on recently released reanalysis datasets and diabatic heating estimates based on Tropical Rainfall Measuring Mission (TRMM), the authors have conducted a composite analysis of vertical anomalous heating structures associated with the Madden–Julian oscillation (MJO). Because diabatic heating lies at the heart of prevailing MJO theories, the intention of this effort is to provide new insights into the fundamental physics of the MJO. However, some discrepancies in the composite vertical MJO heating profiles are noted among the datasets, particularly between three reanalyses and three TRMM estimates. A westward tilting with altitude in the vertical heating structure of the MJO is clearly evident during its eastward propagation based on three reanalysis datasets, which is particularly pronounced when the MJO migrates from the equatorial eastern Indian Ocean (EEIO) to the western Pacific (WP). In contrast, this vertical tilt in heating structure is not readily seen in the three TRMM products. Moreover, a transition from a shallow to deep heating structure associated with the MJO is clearly evident in a pressure–time plot over both the EEIO and WP in three reanalysis datasets. Although this vertical heating structure transition is detectable over the WP in two TRMM products, it is weakly defined in another dataset over the WP and in all three TRMM datasets over the EEIO. The vertical structures of radiative heating QR associated with the MJO are also analyzed based on TRMM and two reanalysis datasets. A westward vertical tilt in QR is apparent in all these datasets: that is, the low-level QR is largely in phase of convection, whereas QR in the upper troposphere lags the maximum convection. The results also suggest a potentially important role of radiative heating for the MJO, particularly over the Indian Ocean. Caveats in heating estimates based on both the reanalysis datasets and TRMM are briefly discussed.

Issues of spatial correlation arising from the use of TRMM rainfall estimates in the Brazilian Amazon

This paper discusses issues arising when Tropical Rainfall Measuring Mission (TRMM) estimates of rainfall characteristics are calculated for the sparsely gauged Brazilian Amazon. The particular issue considered is the calculation of statistical uncertainty in (1) comparisons between TRMM estimates of rainfall characteristics and their corresponding rain gauge estimates and (2) interpolated estimates of rainfall characteristics, which are conditional on local TRMM estimates near ungauged sites. Three characteristics selected for particular comparison are (1) mean annual rainfall, 1998–2005, (2) mean value, over these years, of the 95% quantile of within‐year daily rainfall, and (3) mean number of days in the year with rainfall of 2 mm or more. The paper demonstrates how positive spatial correlation leads to underestimation of the uncertainty in the comparisons (i.e., the standard errors of differences), so that the significance of differences, when observed, is likely to be overestimated if spatial correlation is ignored. For the three characteristics mentioned, the Matérn spatial correlation function with shape parameter κ = 1/2, giving an exponential correlation function, was found to be adequate for modeling spatial correlation. It is also argued that the statistic 1 − b, where b is the slope of the regression of a rain gauge estimate of a rainfall characteristic on its counterpart derived from satellite instrumentation, is a measure of the errors in the satellite (spatially averaged) estimates. Finally, although issues of spatial correlation are discussed with particular reference to rainfall, it is argued that the same issues arise whenever satellite‐derived estimates of a component of the water cycle must be combined with ground‐based measurements.

The Skill of ECMWF Medium-Range Forecasts during the Year of Tropical Convection 2008

Abstract This study uses the European Centre for Medium-Range Weather Forecasts (ECMWF) model-generated high-resolution 10-day-long predictions for the Year of Tropical Convection (YOTC) 2008. Precipitation forecast skills of the model over the tropics are evaluated against the Tropical Rainfall Measuring Mission (TRMM) estimates. It has been shown that the model was able to capture the monthly to seasonal mean features of tropical convection reasonably. Northward propagation of convective bands over the Bay of Bengal was also forecasted realistically up to 5 days in advance, including the onset phase of the monsoon during the first half of June 2008. However, large errors exist in the daily datasets especially for longer lead times over smaller domains. For shorter lead times (less than 4–5 days), forecast errors are much smaller over the oceans than over land. Moreover, the rate of increase of errors with lead time is rapid over the oceans and is confined to the regions where observed precipitation shows large day-to-day variability. It has been shown that this rapid growth of errors over the oceans is related to the spatial pattern of near-surface air temperature. This is probably due to the one-way air–sea interaction in the atmosphere-only model used for forecasting. While the prescribed surface temperature over the oceans remain realistic at shorter lead times, the pattern and hence the gradient of the surface temperature is not altered with change in atmospheric parameters at longer lead times. It has also been shown that the ECMWF model had considerable difficulties in forecasting very low and very heavy intensity of precipitation over South Asia. The model has too few grids with “zero” precipitation and heavy (>40 mm day−1) precipitation. On the other hand, drizzle-like precipitation is too frequent in the model compared to that in the TRMM datasets. Further analysis shows that a major source of error in the ECMWF precipitation forecasts is the diurnal cycle over the South Asian monsoon region. The peak intensity of precipitation in the model forecasts over land (ocean) appear about 6 (9) h earlier than that in the observations. Moreover, the amplitude of the diurnal cycle is much higher in the model forecasts compared to that in the TRMM estimates. It has been seen that the phase error of the diurnal cycle increases with forecast lead time. The error in monthly mean 3-hourly precipitation forecasts is about 2–4 times of the error in the daily mean datasets. Thus, effort should be given to improve the phase and amplitude forecast of the diurnal cycle of precipitation from the model.

Improving Multimodel Forecasts of the Vertical Distribution of Heating Using the TRMM Profiles

Abstract Recently the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM) project office made available a new product called the convective–stratiform heating (CSH). These are the datasets for vertical profiles of diabatic heating rates (the apparent heat source). These observed estimates of heating are obtained from the TRMM satellite’s microwave radiances and the precipitation radar. The importance of such datasets for defining the vertical distribution of heating was largely the initiative of Dr. W.-K. Tao from NASA’s Goddard Laboratory. The need to examine how well some of the current cumulus parameterization schemes perform toward describing the amplitude and the three-dimensional distributions of heating is addressed in this paper. Three versions of the Florida State University (FSU) global atmospheric model are run that utilize different versions of cumulus parameterization schemes; namely, modified Kuo parameterization, simple Arakawa–Schubert parameterization, and Zhang–McFarlane parameterization. The Kuo-type scheme used here relies on moisture convergence and tends to overestimate the rainfall generally compared to the TRMM estimates. The other schemes used here show only a slight overestimate of rain rates compared to TRMM; those invoke mass fluxes that are less stringent in this regard in defining cloud volumes. The mass flux schemes do carry out a total moisture budget for a vertical column model and include all components of the moisture budget and are not limited to the horizontal convergence of moisture. The authors carry out a numerical experimentation that includes over a hundred experiments from each of these models; these experiments differ only in their use of the cumulus parameterization. The rest of the model physics, resolution, and initial states are kept the same for each set of 117 forecasts. The strategy for this experimentation follows the authors’ previous studies with the FSU multimodel superensemble. This includes a 100-day training and a 17-day forecast phase, both of which include a large number of forecast experiments. The training phase provides a useful statistical database for tagging the systematic errors of the respective models. The forecast phase is designed to minimize the collective bias errors of these member models. In these forecasts the authors also include the ensemble mean and the multimodel superensemble. In this paper the authors examine model errors in their representations of the heating (amplitude, vertical level of maximum, and the geographical distributions). The main message of this study is that some cumulus parameterization schemes overestimate the amplitude of heating, whereas others carry lower values. The models also exhibit large errors in the placement of the vertical level of maximum heating. Some significant errors were also found in the geographical distributions of heating. The ensemble mean largely mimics the model features and also carries some large errors. The superensemble is more selective in reducing the three-dimensional collective bias errors of the models and provides the best short range forecasts, through hour 60, for the heating. This study shows that it is possible to diagnose some of the modeling errors in the heating for individual member models and that information can be important for correcting such features.