TRMM 3B42 Research Articles

Reprocessed, Bias-Corrected CMORPH Global High-Resolution Precipitation Estimates from 1998

Abstract The Climate Prediction Center (CPC) morphing technique (CMORPH) satellite precipitation estimates are reprocessed and bias corrected on an 8 km × 8 km grid over the globe (60°S–60°N) and in a 30-min temporal resolution for an 18-yr period from January 1998 to the present to form a climate data record (CDR) of high-resolution global precipitation analysis. First, the purely satellite-based CMORPH precipitation estimates (raw CMORPH) are reprocessed. The integration algorithm is fixed and the input level 2 passive microwave (PMW) retrievals of instantaneous precipitation rates are from identical versions throughout the entire data period. Bias correction is then performed for the raw CMORPH through probability density function (PDF) matching against the CPC daily gauge analysis over land and through adjustment against the Global Precipitation Climatology Project (GPCP) pentad merged analysis of precipitation over ocean. The reprocessed, bias-corrected CMORPH exhibits improved performance in representing the magnitude, spatial distribution patterns, and temporal variations of precipitation over the global domain from 60°S to 60°N. Bias in the CMORPH satellite precipitation estimates is almost completely removed over land during warm seasons (May–September), while during cold seasons (October–April) CMORPH tends to underestimate the precipitation due to the less-than-desirable performance of the current-generation PMW retrievals in detecting and quantifying snowfall and cold season rainfall. An intercomparison study indicated that the reprocessed, bias-corrected CMORPH exhibits consistently superior performance than the widely used TRMM 3B42 (TMPA) in representing both daily and 3-hourly precipitation over the contiguous United States and other global regions.

Are satellite products good proxies for gauge precipitation over Singapore?

The uncertainties in two high-resolution satellite precipitation products (TRMM 3B42 v7.0 and GSMaP v5.222) were investigated by comparing them against rain gauge observations over Singapore on sub-daily scales. The satellite-borne precipitation products are assessed in terms of seasonal, monthly and daily variations, the diurnal cycle, and extreme precipitation over a 10-year period (2000–2010). Results indicate that the uncertainties in extreme precipitation is higher in GSMaP than in TRMM, possibly due to the issues such as satellite merging algorithm, the finer spatio-temporal scale of high intensity precipitation, and the swath time of satellite. Such discrepancies between satellite-borne and gauge-based precipitations at sub-daily scale can possibly lead to distorting analysis of precipitation characteristics and/or application model results. Overall, both satellite products are unable to capture the observed extremes and provide a good agreement with observations only at coarse time scales. Also, the satellite products agree well on the late afternoon maximum and heavier rainfall of gauge-based data in winter season when the Intertropical Convergence Zone (ITCZ) is located over Singapore. However, they do not reproduce the gauge-observed diurnal cycle in summer. The disagreement in summer could be attributed to the dominant satellite overpass time (about 14:00 SGT) later than the diurnal peak time (about 09:00 SGT) of gauge precipitation. From the analyses of extreme precipitation indices, it is inferred that both satellite datasets tend to overestimate the light rain and frequency but underestimate high intensity precipitation and the length of dry spells. This study on quantification of their uncertainty is useful in many aspects especially that these satellite products stand scrutiny over places where there are no good ground data to be compared against. This has serious implications on climate studies as in model evaluations and in particular, climate model simulated future projections, when information on precipitation extremes need to be reliable as they are highly crucial for adaptation and mitigation.

Intermittency in Precipitation: Duration, Frequency, Intensity, and Amounts Using Hourly Data

Abstract Intermittency is a core characteristic of precipitation, not well described by data and very poorly modeled. Detailed analyses are made of near-global gridded (about 1°) hourly or 3-hourly precipitation rates from two updated observational datasets [3-hourly TRMM 3B42, version 7, and hourly CMORPH, version 1.0, bias corrected (CRT)] and from special runs of CESM from January 1998 to December 2013 to obtain hourly values. The analyses explore the intermittency of precipitation: the frequency, intensity, duration, and amounts. A comparison is made for all products using several metrics with a focus on the duration of events, and a new metric is proposed based on the ratio of the frequency of precipitation at certain rates (0.2–2 mm h−1) for hourly versus 3-hourly versus daily data. For all seasons and rain rates, TRMM values are similar in pattern to CMORPH, but durations are about 80%–85%. It is mainly over land in the monsoons that CMORPH exceeds TRMM rain durations. Observed duration of precipitation events in CMORPH over oceans are 12–15 h in the tropics and subtropics, much less than the ~20 h for CESM. Hence, the observational results differ somewhat but both are considerably different from the model, which has too much precipitation overall, and it precipitates far too often at low rates and not enough for intense rates, with the divide about 1–2 mm h−1. There is a need to properly represent precipitation phenomena and processes either explicitly or implicitly (parameterized).

Characterization of droughts during 2001–2014 based on remote sensing: A case study of Northeast China

Northeast China, the most important region for commercial grain farming in China, is vulnerable to drought due to high fluctuation in monthly rainfall. Timely, accurate and effective drought monitoring is very essential for securing the output of grain farming. In this study, three widely used drought indices were compared using satellite soil moisture data and their capability for drought monitoring was evaluated in Northeast China. Three indices are Normalized Monthly Precipitation Anomaly Percentage (NPA), Vegetation Health Index (VHI) and Normalized Vegetation Supply Water Index (NVSWI). In order to know the relationship between rainfall and drought especially over different local climate zones and give a more detail strategy for crop irrigation, a time-lag relationship is investigated based on the TRMM 3B43 monthly precipitation and NVSWI. Tendency rate (slope) was used to characterize the change of drought events in the region for the period 2001–2014. Results showed that: (1) the three selected indices were suitable for drought monitoring, with NVSWI performing the best in terms of the highest correlation with soil moisture, (2) an obvious time-lag was observed between rainfall and drought with the time lag being one month for all three climate zones, (3) drought occurred more frequently in spring and winter, while in summer drought occurred more easily in the west than in the east in Northeast China, (4) overall the frequency of drought was decreasing from 2001 to 2014 in Northeast China.

Rainfall Extremes: a Novel Modeling Approach for Regionalization

The rainfall events of extreme magnitude over the past few decades have caused destructive damages to lives and properties, especially in the subcontinent (e.g. Pakistan, India, Bangladesh etc). Rainfall hazard maps for these areas can be of great practical and theoretical interests. In our work, we used extreme value analysis and spatial interpolation techniques to provide such maps through a combination of the Tropical Rainfall Measuring Mission Precipitation (TRMM) 3B42 product and raingauge data. This mixed approach takes advantage of both the long time series available at a limited number of stations, and the large spatial coverage of the satellite data which, instead, has a poor temporal extent. The methodology is implemented by (1) creating a unique growth curve for the homogeneous region by utilizing in-situ rainfall data and (2) mapping the parameters of intensity-duration functions for the entire length of the study area by using TRMM 3B42 product. The regional results obtained by using mixed approach and TRMM 3B42 are compared with the estimates obtained by using in-situ data. The comparison showed that the overall output of mixed approach is more consistent with what transpired by in-situ data for a pre-defined return period.

Characteristics of wet and dry spells in the West African monsoon system

AbstractUsing 17 years (1998–2014) of daily TRMM 3B42 rainfall data, we provide a climatological characterization of wet and dry spells in West Africa, which should serve to assess the ability of climate model to simulate these high impact events. The study focuses on four subregions (Western and Central Sahel, Sudanian zone and Guinea Coast). Defining wet (dry) spells as sequences of consecutive days with precipitation higher (lower) than 1 mm, we describe the space‐time variability of wet and dry spell occurrence. This climatology stresses the influence of the relief on the number and duration of these spells. The spatio‐temporal variability of the wet and dry spells also appears to be closely related to the spatio‐temporal variability of the West African monsoon. The number of wet spells of all durations and of 2–3 day dry spells have similar features with a maximum occurrence during the local rainy seasons and a spatial pattern similar to the mean annual rainfall with a north–south gradient. In contrast, dry spells lasting more than four days show some singularities such as a low occurrence over the Sahelian band or high occurrence along the Guinea Coast mainly from Ivory Coast to Benin. Moreover, the seasonal cycle of these longer dry spells presents higher occurrences at the beginning and the end of the rainy seasons.

Climate change driven water budget dynamics of a Tibetan inland lake

Understanding the hydrologic processes of inland lake basins in the Tibetan Plateau (TP) could provide insights into the responses of Tibetan lake dynamics to climate change. An efficient approach for this purpose is to represent complex hydrologic behaviors of such Tibetan lake watersheds with plausible hydrologic models. In this study, water level fluctuations of Lake Nam Co, an inland lake in the central TP, were investigated using a lumped lake-watershed model. The degree-day factor method was introduced to improve the model applicability in glacier-covered basins. The model simulated the hydrologic processes as well as the lake water budget. Remote sensing images (Landsat MSS, TM, ETM+ and OLI) from 1972 to 2015 were used to identify the glacier and lake boundaries. Multisource climate data (e.g., ground point observation, 0.25o gridded APHRODITE and TRMM 3B42 v7 precipitation products) were used to drive the hydrologic model at a monthly time step. Results of trend analysis showed that basin-wide annual air temperature increased by the rate 0.04°C/yr from 1961 to 2015. Mean annual precipitation slowly increased from 1961 to the mid-1990s, and then rapidly increased from the late-1990s to the mid-2000s, and finally obviously decreased after the mid-2000s. As a response to climate change, glaciers decreased by 62.69km2 (29%) and lake area increased by 91.83km2 (4.7%) from 1972 to 2015. The analysis of lake water budget suggested that, the total basin runoff and on-lake precipitation contributed 1.36km3/yr (66%) and 0.7km3/yr (34%), respectively, to mean annual water gain of the lake. Glacier runoff was 14% of the basin runoff and 10% of the total water gain of the lake. The percentages of lake evaporation, water seepage and water surplus were 65%, 20% and 15%, respectively. Lake level increased with the rate of 0.14m/yr for the study period 1961–2015. It could be concluded that precipitation was the dominant controlling factor for the different magnitudes of lake level rising rates of 0.10, 0.41 and 0.06m/yr for the periods of 1961–1998, 1999–2008 and 2009–2015, respectively.

Intercomparisons of Rainfall Estimates from TRMM and GPM Multisatellite Products over the Upper Mekong River Basin

Abstract The aim of this study is to evaluate the accuracy of daily rainfall estimates based on the GPM level-3 final product derived from the IMERG algorithm (abbreviated as IMERG) and TRMM 3B42, version 7 (abbreviated as 3B42), in the upper Mekong River basin, a mountainous region in southwestern China. High-density rain gauges provide exceptional resources for ground validation of satellite rainfall estimates over this region. The performance of the two satellite rainfall products is evaluated during two rainy seasons (May–October) over the period 2014–15, as well as their applications in hydrological simulations. Results indicate that 1) IMERG systematically reduces the bias value in rainfall estimates at the gridbox scale and presents a greater ability to capture rainfall variability at the local domain scale compared with 3B42; 2) IMERG improves the ability to capture rain events with moderate intensities and presents higher capability in detecting occurrences of extreme rain events, but significantly overestimates the amounts of these extreme events; and 3) IMERG generally produces comparable daily streamflow simulations to 3B42 and tends to outperform 3B42 in driving hydrological simulations when calibrating model parameters using each rainfall input. This study provides an early evaluation of the IMERG rainfall product over a mountainous region. The findings indicate the potential of the IMERG product in overestimating extreme rain events, which could serve as the basis for further improvement of IMERG rainfall retrieval algorithms. The hydrological evaluations described here could shed light on the emerging application of retrospectively generated IMERG products back to the TRMM era.

Evaluation of TRMM 3B42 V7 Rainfall Product over the Oum Er Rbia Watershed in Morocco

In arid and semi-arid areas, rainfall is often characterized by a strong spatial and temporal variability. These environmental factors, combined with the sparsity of the measurement networks in developing countries, constitute real constraints for water resources management. In recent years, several spatial rainfall measurement sources have become available, such as TRMM data (Tropical Rainfall Measurement Mission). In this study, the TRMM 3B42 Version 7 product was evaluated using rain gauges measurements from 19 stations in the Oum-Er-Bia (OER) basin located in the center of Morocco. The relevance of the TRMM product was tested by direct comparison with observations at different time scales (daily, monthly, and annual) between 1998 and 2010. Results show that the satellite product provides poor estimations of rainfall at the daily time scale giving an average Pearson correlation coefficient (r) of 0.2 and average Root Mean Square Error (RMSE) of 10 mm. However, the accuracy of TRMM rainfall is improved when temporally averaged to monthly time scale (r of 0.8 and RMSE of 28 mm) or annual time scale (r of 0.71 and RMSE of 157 mm). Moreover, improved correlation with observed data was obtained for data spatially averaged at the watershed scale. Therefore, at the monthly and annual time scales, TRMM data can be a useful source of rainfall data for water resources monitoring and management in ungauged basins in semi-arid regions.

Downscaling of Open Coarse Precipitation Data through Spatial and Statistical Analysis, Integrating NDVI, NDWI, Elevation, and Distance from Sea

This study aims to improve the statistical spatial downscaling of coarse precipitation (TRMM 3B43 product) and also to explore its limitations in the Mediterranean area. It was carried out in Morocco and was based on an open dataset including four predictors (NDVI, NDWI, DEM, and distance from sea) that explain TRMM 3B43 product. For this purpose, four groups of models were established based on different combinations of the four predictors, in order to compare from one side NDVI and NDWI based models and the other side stepwise with multiple regression. The models that have given rise to the best approximations and best fits were used to downscale TRMM 3B43 product. The resulting downscaled and calibrated precipitations were validated by independent RGS. Aside from that, the limitations of the proposed approach were assessed in five bioclimatic stages. Furthermore, the influence of the sea was analyzed in five classes of distance. The findings showed that the models built using NDVI and NDWI have a high correlation and therefore can be used to downscale precipitation. The integration of elevation and distance improved the correlation models. According to R2, RMSE, bias, and MAE, the study revealed that there is a great agreement between downscaled precipitations and RGS measurements. In addition, the analysis showed that the contribution of the variable (distance from sea) is evident around the coastal area and decreases progressively. Likewise, the study demonstrated that the approach performs well in humid and arid bioclimatic stages compared to others.

Spatial downscaling of TRMM‐based precipitation data using vegetative response in Xinjiang, China

ABSTRACTStation‐based observed precipitation data are available in a limited way and only at low spatial resolution. High‐resolution satellite rainfall products can help to monitor precipitation changes over large areas. The Tropical Rainfall Measuring Mission 3B43 (TRMM 3B43) precipitation data with coarse spatial resolution and low data accuracy are capable of depicting the spatial variability of precipitation, but fail to estimate the accurate magnitude. These data available in Xinjiang, China, need to be evaluated, especially because Xinjiang has a complex terrain and application of such data becomes a challenging task. Based on the relation between precipitation and normalized difference vegetation index (NDVI), a proxy for vegetation, a new statistical downscaling algorithm is proposed in this study. The calibration was based on geographical difference analysis (GDA) and the monthly fractions derived from the un‐calibrated TRMM data can be used to disaggregate high‐resolution annual precipitation to high‐resolution monthly precipitation. The accuracy of downscaled TRMM precipitation was evaluated based on station‐based observed precipitation for a period of 1998–2010. Results indicated that: (1) optimal relations shown by R2 between TRMM precipitation and NDVI at different temporal and spatial scales were different: the largest R2 value was 0.69 for the period of 1998–2010 at a spatial resolution of 1.00° × 1.00°, was 0.70 for the dry year (2001 in this study) at a spatial resolution of 0.75° × 0.75°, and was 0.67 for the wet year (2010 in this study) at a spatial resolution of 1.25° × 1.25°; (2) the downscaled TRMM precipitation data obtained using NDVI described spatial patterns of precipitation reasonably well at a spatial resolution of 8 km × 8 km with more detailed information when compared to the raw TRMM precipitation at a spatial resolution of 0.25° × 0.25°; (3) the downscaled TRMM precipitation with GDA calibration, PDSGDA, can exclude the regions with irrigation‐ and/or ground water‐induced vegetation coverage. Therefore, PDSGDA obtained in this study can be regarded as estimated alternative precipitation data across Xinjiang for management of water resources and agricultural irrigation activities.

PEMANFAATAN DATA SATELIT TROPICAL RAINFALL MEASURING MISSION (TRMM) UNTUK PEMETAAN ZONA AGROKLIMAT OLDEMAN DI KALIMANTAN SELATAN

The irregularity of observation sites distribution and network density, lack data availability and discontinuity are the obstacles to analyzing and producing the information of agroclimate zone in South Kalimantan. TRMM satellite needs to be researched to overcome the limitations of surface observation data. This study intended to validate TRMM 3B43 satellite data with surface rainfall, to produce Oldeman agroclimate zone based on TRMM satellite data and to analyze the agroclimate zone for agricultural resources management. Data validation is done using the statistical method by analyzing the correlation value (r) and RMSE (Root Mean Square Error). The agroclimate zone is classified based on Oldeman climate classification type. The calculation results are mapped spatially using Arc GIS 10.2 software. The validation result of the TRMM satellite and surface rainfall data shows a high correlation value for the monthly average. The value of correlation coefficient is 0,97 and 25 mm for RMSE value. Oldeman agroclimate zone based on TRMM satellite data in south Kalimantan is divided into five climate zones, such as B1, B2, C1, C2, and D1.

A Comparison of Different Regression Algorithms for Downscaling Monthly Satellite-Based Precipitation over North China

Environmental monitoring of Earth from space has provided invaluable information for understanding land–atmosphere water and energy exchanges. However, the use of satellite-based precipitation observations in hydrologic and environmental applications is often limited by their coarse spatial resolutions. In this study, we propose a downscaling approach based on precipitation–land surface characteristics. Daytime land surface temperature, nighttime land surface temperature, and day–night land surface temperature differences were introduced as variables in addition to the Normalized Difference Vegetation Index (NDVI), the Digital Elevation Model (DEM), and geolocation (longitude, latitude). Four machine learning regression algorithms, the classification and regression tree (CART), the k-nearest neighbors (k-NN), the support vector machine (SVM), and random forests (RF), were implemented to downscale monthly TRMM 3B43 V7 precipitation data from 25 km to 1 km over North China for the purpose of comparison of algorithm performance. The downscaled results were validated based on observations from meteorological stations and were also compared to a previous downscaling algorithm. According to the validation results, the RF-based model produced the results with the highest accuracy. It was followed by SVM, CART, and k-NN, but the accuracy of the downscaled results using SVM relied greatly on residual correction. The downscaled results were well correlated with the observations during the year, but the accuracies were relatively lower in July to September. Downscaling errors increase as monthly total precipitation increases, but the RF model was less affected by this proportional effect between errors and observation compared with the other algorithms. The variable importances of the land surface temperature (LST) feature variables were higher than those of NDVI, which indicates the significance of considering the precipitation–land surface temperature relationship when downscaling TRMM 3B43 V7 precipitation data.

Accuracy evaluation of multi-satellite precipitation products over Circum-Bohai-Sea Region.

Precipitation is a key variable of the global energy and water cycle. The accuracy of precipitation data is essential for monitoring and forecasting flood and drought, as well as water resources management. Based on daily precipitation data of 55 meteorological stations over Circum-Bohai-Sea region during 1998-2014, the accuracy was evaluated and compared among 3 satellite-based precipitation products, i.e., TRMM 3B42 V7, CMORPH, and PERSIANN from daily to monthly scale. Their errors were quantified for different seasons. Results showed that generally, TRMM 3B42 V7 had the best performance while PERSIANN had the largest error. However, CMORPH had the highest detection rate of rainfall events. The rainfall was slightly overestimated by 3B42 V7 but underestimated by CMORPH and PERSIANN. Seasonally, the skill score in summer was higher than that in other seasons, and the capturing capability was much lower in winter. Meanwhile, the correlation coefficient and root mean square error varied with seasons. The performance of 3 satellite-based precipitation products increased from the daily to monthly scale. Larger errors still existed in the daily rainfall estimates, and especially the rainstorm was seriously underestimated. This study provided a basis for the use of satellite precipitation products in precipitation estimation and its application in climate and hydrological simulation over Circum-Bohai-Sea region.

A Spatial Downscaling Algorithm for Satellite-Based Precipitation over the Tibetan Plateau Based on NDVI, DEM, and Land Surface Temperature

Precipitation is an important controlling parameter for land surface processes, and is crucial to ecological, environmental, and hydrological modeling. In this study, we propose a spatial downscaling approach based on precipitation–land surface characteristics. Land surface temperature features were introduced as new variables in addition to the Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM) to improve the spatial downscaling algorithm. Two machine learning algorithms, Random Forests (RF) and support vector machine (SVM), were implemented to downscale the yearly Tropical Rainfall Measuring Mission 3B43 V7 (TRMM 3B43 V7) precipitation data from 25 km to 1 km over the Tibetan Plateau area, and the downscaled results were validated on the basis of observations from meteorological stations and comparisons with previous downscaling algorithms. According to the validation results, the RF and SVM-based models produced higher accuracy than the exponential regression (ER) model and multiple linear regression (MLR) model. The downscaled results also had higher accuracy than the original TRMM 3B43 V7 dataset. Moreover, models including land surface temperature variables (LSTs) performed better than those without LSTs, indicating the significance of considering precipitation–land surface temperature when downscaling TRMM 3B43 V7 precipitation data. The RF model with only NDVI and DEM produced much worse accuracy than the SVM model with the same variables. This indicates that the Random Forests algorithm is more sensitive to LSTs than the SVM when downscaling yearly TRMM 3B43 V7 precipitation data over Tibetan Plateau. Moreover, the precipitation–LSTs relationship is more instantaneous, making it more likely to downscale precipitation at a monthly or weekly temporal scale.

A new method to compare hourly rainfall between station observations and satellite products over central-eastern China

This study employs a newly defined regional-rainfall-event (RRE) concept to compare the hourly characteristics of warm-season (May-September) rainfall among rain gauge observations, China merged hourly precipitation analysis (CMPA-Hourly), and two commonly used satellite products (TRMM 3B42 and CMORPH). By considering the rainfall characteristics in a given limited area rather than a single point or grid, this method largely eliminates the differences in rainfall characteristics among different observations or measurements over central-eastern China. The results show that the spatial distribution and diurnal variation of RRE frequency and intensity are quite consistent among different datasets, and the performance of CMPA-Hourly is better than the satellite products when compared with station observations. A regional rainfall coefficient (RRC), which can be used to classify local rain and regional rain, is employed to represent the spatial spread of rainfall in the limited region defining the RRE. It is found that rainfall spread in the selected grid box is more uniform during the nocturnal to morning hours over central-eastern China. The RRC tends to reach its diurnal maximum several hours after the RRE intensity peaks, implying an intermediate transition stage from convective to stratiform rainfall. In the afternoon, the RRC reaches its minimum, implying the dominance of local convections on small spatial scale in those hours, which could cause large differences in rain gauge and satellite observations. Since the RRE method reflects the overall features of rainfall in a limited region rather than at a fixed point or in a single grid, the widely recognized overestimation of afternoon rainfall in satellite products is not obvious, and thus the satellite estimates are more reliable in representing sub-daily variation of rainfall from the RRE perspective. This study proposes a reasonable method to compare satellite products with rain gauge observations on the sub-daily scale, which also has great potential to be used in evaluating the spatiotemporal variation of cloud and rainfall in numerical models.

Adaptability evaluation of TRMM over the Tianshan Mountains in central Asia

Accurate precipitation in mountain area is very important for evaluating the hydrological process and ecological problem. With the satellite data having been widely used in the past few decades, adaptability evaluation becomes the principle problem. The adaptability of TRMM 3B43 in mountain area of Central Asia was analyzed in this study. The TRMM product was compared with the observed data for the period of 2000-2006. Four statistic parameters were introduced based on the statistical analysis theory. The results show that the bias reached -13.93% over the entire regions, and the correlation coefficients over 70% of stations were greater than 0.70. According to the accuracy analysis of TRMM, we found the errors have significant differences in time and space. On the whole, the precision in the warm seasons is much higher than that in the cold seasons. The precision of the southern and eastern areas is higher than the other areas in space. Additionally, the accuracy of TRMM with elevation was acceptable at very significant level. This study indicates that the precipitation from TRMM 3B43 could be applied in the Tianshan Mountains in Central Asia. It could provide reference for the use of new data source in the mountain area.

VALIDATION OF SATELLITE PRECIPITATION (TRMM 3B43) IN ECUADORIAN COASTAL PLAINS, ANDEAN HIGHLANDS AND AMAZONIAN RAINFOREST

Precipitation monitoring is of utmost importance for water resource management. However, in regions of complex terrain such as Ecuador, the high spatio-temporal precipitation variability and the scarcity of rain gauges, make difficult to obtain accurate estimations of precipitation. Remotely sensed estimated precipitation, such as the Multi-satellite Precipitation Analysis TRMM, can cope with this problem after a validation process, which must be representative in space and time. In this work we validate monthly estimates from TRMM 3B43 satellite precipitation (0.25° x 0.25° resolution), by using ground data from 14 rain gauges in Ecuador. The stations are located in the 3 most differentiated regions of the country: the Pacific coastal plains, the Andean highlands, and the Amazon rainforest. Time series, between 1998 – 2010, of imagery and rain gauges were compared using statistical error metrics such as bias, root mean square error, and Pearson correlation; and with detection indexes such as probability of detection, equitable threat score, false alarm rate and frequency bias index. The results showed that precipitation seasonality is well represented and TRMM 3B43 acceptably estimates the monthly precipitation in the three regions of the country. According to both, statistical error metrics and detection indexes, the coastal and Amazon regions are better estimated quantitatively than the Andean highlands. Additionally, it was found that there are better estimations for light precipitation rates. The present validation of TRMM 3B43 provides important results to support further studies on calibration and bias correction of precipitation in ungagged watershed basins.

VALIDATION OF SATELLITE PRECIPITATION (TRMM 3B43) IN ECUADORIAN COASTAL PLAINS, ANDEAN HIGHLANDS AND AMAZONIAN RAINFOREST

Abstract. Precipitation monitoring is of utmost importance for water resource management. However, in regions of complex terrain such as Ecuador, the high spatio-temporal precipitation variability and the scarcity of rain gauges, make difficult to obtain accurate estimations of precipitation. Remotely sensed estimated precipitation, such as the Multi-satellite Precipitation Analysis TRMM, can cope with this problem after a validation process, which must be representative in space and time. In this work we validate monthly estimates from TRMM 3B43 satellite precipitation (0.25° x 0.25° resolution), by using ground data from 14 rain gauges in Ecuador. The stations are located in the 3 most differentiated regions of the country: the Pacific coastal plains, the Andean highlands, and the Amazon rainforest. Time series, between 1998 – 2010, of imagery and rain gauges were compared using statistical error metrics such as bias, root mean square error, and Pearson correlation; and with detection indexes such as probability of detection, equitable threat score, false alarm rate and frequency bias index. The results showed that precipitation seasonality is well represented and TRMM 3B43 acceptably estimates the monthly precipitation in the three regions of the country. According to both, statistical error metrics and detection indexes, the coastal and Amazon regions are better estimated quantitatively than the Andean highlands. Additionally, it was found that there are better estimations for light precipitation rates. The present validation of TRMM 3B43 provides important results to support further studies on calibration and bias correction of precipitation in ungagged watershed basins.

Evolution of the Diurnal Precipitation Cycle with the Passage of a Madden–Julian Oscillation Event through the Maritime Continent

Changes in the diurnal precipitation cycle as the Madden–Julian oscillation (MJO) propagates through the Maritime Continent are investigated to explore the processes behind seaward-propagating precipitation northeast of New Guinea. Satellite rainfall estimates from TRMM 3B42 and the Climate Prediction Center morphing technique (CMORPH) are combined with simulations from the Weather Research and Forecasting (WRF) Model with a horizontal resolution of 4 km. Comparison with 24-h rain gauge measurements indicates that both satellite estimates and the WRF Model exhibit systematic biases. Despite these biases, the changing patterns of offshore precipitation with the passage of the MJO show good consistency between satellite estimates and the WRF Model. In the few days prior to the main MJO envelope, light background wind, relatively clear skies, and an increasingly moist environment promote favorable conditions for the diurnal precipitation cycle. Two distinct processes are identified: 100–200 km from the coast, precipitation moves offshore as a squall line with a propagation speed of 3–5 m s−1. Farther offshore, precipitation propagates with a speed close to 18 m s−1and is associated with an inertia–gravity wave generated by diurnally oscillating heating from radiative and moist convective processes over the land. A gravity wave signature is evident even after the MJO active period when there is little precipitation. By correcting for the background flow perpendicular to the coast, potential temperature anomalies for the lead-up, active, and follow-on MJO periods are shown to collapse to a remarkably invariant shape for a given time of day.