Multi-satellite Precipitation Estimates Research Articles

Which Error Components in TRMM-Based Multisatellite Precipitation Estimates Reduce over Chinese Mainland after Official Bias Adjustments: Systematic or Random?

Abstract Climatological calibration algorithm (CCA) and satellite–gauge combination (SG) are two official bias adjustments for satellite precipitation estimates (SPE) in the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA). The CCA is designed for the near-real-time SPEs, while the SG procedure is a final step to merge pure SPEs with gauge observations. This study explored the impacts of CCA and SG on the systematic and random errors of TMPA SPEs. The errors of TMPA version-7 near-real-time products before and after CCA (RT_UC, RT_C), and the research product TMPA 3B42 (V7), were decomposed into systematic and random components, benchmarked by the China Gauge-based Daily Precipitation Analysis (CGDPA). After being calibrated by CCA, RT_C reduced the systematic errors relative to RT_UC over the Chinese mainland, except in the Tibetan Plateau and Tianshan Mountains. However, CCA did not aid in reducing random errors; instead, it even exacerbated the random errors. On the other hand, the SG merging is more effective in reducing systematic errors of SPEs than CCA calibration because of the direct inclusion of simultaneous gauge data from the Global Precipitation Climatology Centre (GPCC). We also found that SG merging reduced the random errors of pure SPEs over regions with relatively higher elevations. Despite lower random errors in V7 over the complex terrain region, the SG unfavorably increased the random errors over southeastern China. The results reported here may offer valuable insights into the effects of CCA and SG techniques drawn from TMPA, with the potential to advance the development of bias-adjusting algorithms for SPEs in the future.

A Novel Real-Time Error Adjustment Method With Considering Four Factors for Correcting Hourly Multi-Satellite Precipitation Estimates

High-accuracy near-real-time satellite precipitation estimates (SPEs) provide an opportunity for hydrometeorologists to improve the forecasting of extreme events, such as flood, landslide, tropical cyclone, and other extreme events, at the large scale. However, the currently operational near-real-time SPEs still have larger errors and uncertainties. In this study, we found that there exists a clear relationship of spatial plane function (SPF) between retrieval errors of SPEs and four crucial factors including topography, seasonality, climate type, and rain rate. Based on this finding, we proposed a novel error adjustment method to correct the near-real-time hourly global satellite mapping of precipitation (GSMaP-NRT) estimates in real-time. The new satellite precipitation dataset, namely, ILSF-RT, was then inter-compared with the latest near-real-time GSMaP product suite (i.e., GSMaP-NRT and GSMaP-Gauge-NRT). Verification results show that the proposed method can effectively reduce the retrieval errors of GSMaP-NRT for various terrains and rain rates over different seasons and climate-type areas. The new ILSF-RT even exhibits a general improvement over the GSMaP-Gauge-NRT estimates. Furthermore, one important merit of the new method is that it can perform rather well in validation even when not much historical data were applied as training samples in calibration, for example, during the generation of ILSF-RT, only 45 data pairs of satellite retrievals and ground observations were used for winter season over Chinese arid areas. However, the results of bias score show that the current method seems unsuitable to adjust the rainfall events with higher rain rates (>=1 mm hr^−1), which needs to be further improved.

Real-time bias adjustment for satellite-based precipitation estimates over Mainland China

An improved cumulative distribution function (CDF)-based approach to reduce the systematic biases of multi-satellite precipitation estimates in real time is proposed and verified over Mainland China. Efforts are primarily focused on establishing the bias-adjusting model by adopting the CDF based on a Self-adaptive Moving Window (CSMW), which systematically integrates the China Gauge-based Daily Precipitation Analysis (CGDPA) into the real-time TRMM Multisatellite Precipitation Analysis (TMPA-RT). In our modelling experiments, the first 9-yr (2008–2016) precipitation data pairs were used to calibrate the CSMW model and establish a satellite-gauge relationship, which was then applied to the last 3 years of 2017–2019 as validation. Assessment results during the independent validation period show that the CSMW approach can significantly reduce the systematic positive bias of original TMPA-RT precipitation estimates in that the relative bias (RB) during the validation period decreases from 16.01% before adjustments to −0.29%, and the root-mean-square error (RMSE) also has a dramatic drop of 13%. The error component analysis indicates that the substantial improvement is mainly manifested in the hit events (observed rain was correctly detected by satellite) but it failed to reduce the miss bias (observed rain was not detected by satellite). This arises because a majority of missed precipitation is drizzle and falls below the rain/no-rain discriminant threshold, which is normally excluded from the CSMW algorithm. Additionally, the CSMW approach seems to have significantly improved the TMPA-RT estimates at the medium-high rain rates (>8 mm/day), but it also has a limitation in enhancing the correlation coefficient between satellite retrievals and ground observations. The major advantage of this approach is its applicability when real-time gauge data are not available, which could further facilitate the expansion of satellite-based precipitation estimates for real-time natural hazards forecasting.

Comparing Station Based and Gridded Rainfall Data for Hydrological Modelling

In the domain of hydrological modelling precipitation is one of the essential parameter as an input variable. Precipitation input as point gauge based rainfall data is commonly to use in hydrological modelling. Point gauge based rainfall data measurement has a very big limitation of spatial coverage of rainfall data over the area. Most of the countries in the world has very poor density of rain gauge network including India. Catastrophic change in the climate condition made hydrological modelling less reliable when it comes to application of point gauge based rainfall. Thus, it is very important to use rainfall record which has a good spatial as well as temporal resolutions. Precipitation measurement has been significantly evolved after introduction of remote sensing technologies. There are very few method/model of satellite driven rainfall, which has very good spatio-temporal gridded resolution of rainfall records. This paper presents a comprehensive review of such multi-satellite precipitation estimates of gridded rainfall data set over the World. The data sets which we have selected has more than three decades of various precipitation data, i.e., Moderate Resolution Imaging Spectroradiometer (MODIS), PERSIANN-Cloud Classification System (PERSIANN-CCS), Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE’s), Climate Prediction Center (CPC), Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Climatology Project (GPCP). The application of satellite based rainfall measurement and its application in hydrological modelling also discussed in this paper, briefly.

Impact of the crucial geographic and climatic factors on the input source errors of GPM-based global satellite precipitation estimates

The impact of crucial geographic and climatic factors on the input source errors of integrated multi-satellite precipitation estimates is an important but still unclear issue for both algorithm developers and data users. This study primarily focused on the impacts of the twelve input sources used in the latest Global Satellite Mapping of Precipitation for Global Precipitation Measurement (GPM-GSMaP) for different climatic regions, elevations, and seasons over mainland China. Our evaluation results show that the error features of the input sources from several passive microwave and infrared sensors are related to the accuracy of GPM-GSMaP precipitation estimates. The input sources show larger hits, misses and false biases in the semi-arid and arid regions, for which the false bias was particularly significant. As for the seasonality, the input data sources exhibit a better performance in summer and have relatively lower hits and higher biases in winter. We also found that precipitation retrievals of the input sources are affected by topography in different extents. In terms of passive microwave sensors, the conical-scanning imagers generally outperform the cross-track-scanning sounders, but the sounders-based precipitation estimates have relatively better detection capability. As one of the core sensors for GPM, the microwave imager GMI reveals inadequacies in representing areal rainfall patterns and has relatively large biases especially in the winter and spring seasons, suggesting that the current GMI algorithm might need to be further improved.

Validation of rainfall data estimated by GPM satellite on Southern Amazon region

Rainfall is a meteorological variable of great importance for hydric balance and for weather studies. Rainfall estimation, carried out by satellites, has increased the climatological dataset related to precipitation. However, the accuracy of these data is questionable. This paper aimed to validate the estimates done by the Global Precipitation Measurement (GPM) satellite for the mesoregion of Southern Amazonas State, Brazil. The surface data were collected by the National Water Agency – ANA and National Institute of Meteorology – INMET, and is available at both institutions’ websites. The satellite precipitation data were accessed directly from the NASA webpage. Statistical analysis of Pearson correlation was used, as well as the Willmott’s “d” index and errors from the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error). The GPM satellite satisfactorily estimated the precipitation, once it had correlations above 73% and high Willmott coefficients (between 0.86 and 0.97). The MAE and RMSE showed values that varied from 36.50 mm to 72.49 mm and 13.81 mm to 71.76 mm, respectively. Seasonal rain variations are represented accordingly. In some cases, either an underestimation or an overestimation of the rain data was observed. In the yearly totals, a high rate of similarity between the estimated and measured values was observed. We concluded that the GPM-based multi-satellite precipitation estimates can be used, even though they are not 100% reliable. However, adjustments in calibration for the region are necessary and recommended.

Inter-Comparison of Rain-Gauge, Radar, and Satellite (IMERG GPM) Precipitation Estimates Performance for Rainfall-Runoff Modeling in a Mountainous Catchment in Poland

Precipitation is one of the essential variables in rainfall-runoff modeling. For hydrological purposes, the most commonly used data sources of precipitation are rain gauges and weather radars. Recently, multi-satellite precipitation estimates have gained importance thanks to the emergence of Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG GPM), a successor of a very successful Tropical Rainfall Measuring Mission (TRMM) mission which has been providing high-quality precipitation estimates for almost two decades. Hydrological modeling of mountainous catchment requires reliable precipitation inputs in both time and space as the hydrological response of such a catchment is very quick. This paper presents an inter-comparison of event-based rainfall-runoff simulations using precipitation data originating from three different sources. For semi-distributed modeling of discharge in the mountainous river, the Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS) is applied. The model was calibrated and validated for the period 2014–2016 using measurement data from the Upper Skawa catchment a small mountainous catchment in southern Poland. The performance of the model was assessed using the Nash–Sutcliffe efficiency coefficient (NSE), Pearson’s correlation coefficient (r), Percent bias (PBias) and Relative peak flow difference (rPFD). The results show that for the event-based modeling adjusted radar rainfall estimates and IMERG GPM satellite precipitation estimates are the most reliable precipitation data sources. For each source of the precipitation data the model was calibrated separately as the spatial and temporal distributions of rainfall significantly impact the estimated values of model parameters. It has been found that the applied Soil Conservation Service (SCS) Curve Number loss method performs best for flood events having a unimodal time distribution. The analysis of the simulation time-steps indicates that time aggregation of precipitation data from 1 to 2 h (not exceeding the response time of the catchment) provide a significant improvement of flow simulation results for all the models while further aggregation, up to 4 h, seems to be valuable only for model based on rain gauge precipitation data.

How accurate are satellite estimates of precipitation over the north Indian Ocean?

Following the launch of the Global Precipitation Measurement (GPM) Core Observatory in early 2014, motivated from the successful Tropical Rainfall Measurement Mission (TRMM) satellite, an advanced and sophisticated global multi-satellite precipitation product–Integrated Multi-satellitE Retrievals for GPM (IMERG) was released at finer spatio-temporal scales. This precipitation product has been upgraded recently after several refinements and supposed to be superior to other existing global or quasi-global multi-satellite precipitation estimates. In the present study, IMERG precipitation is comprehensively evaluated for the first time against moored buoy observations over the north Indian Ocean at hourly scale for the study period of March 2014 to December 2015. IMERG precipitation performs considerably better over the Bay of Bengal than the Arabian Sea in both detection and estimation. The systematic error in IMERG is appreciably lower by about 14%; however, it generally overestimates in-situ precipitation and also exhibits noticeable false alarms. Furthermore, IMERG essentially shows an improvement over the TRMM Multi-satellite Precipitation Analysis (TMPA) at a daily scale over the north Indian Ocean. IMERG precipitation estimates show overall promising error characteristics, but there is still a need of substantial efforts for improvement in the satellite-based precipitation estimation algorithms especially over data sparse regions such as north Indian Ocean.

Error features of the hourly GSMaP multi-satellite precipitation estimates over nine major basins of China

AbstractAs one of the current mainstream satellite precipitation estimates, the Global Satellite Mapping of Precipitation (GSMaP) system of Japan has been developed to produce high-precision and high-resolution global rainfall products by integrating almost all of the available precipitation-related satellite sensors. To quantify the error features of GSMaP estimates and understand their hydrological potentials at short temporal scale, three widely used GSMaP products (GSMaP_NRT, GSMaP_MVK, and GSMaP_Gauge) were comprehensively investigated at 1 hourly and 0.1° × 0.1° resolutions over nine major basins of China. Assessment results show that GSMaP_NRT apparently underestimates the rainfall amounts, while GSMaP_MVK with both forward and backward propagation processes algorithm can effectively capture the most rainfall events and has the lower error and bias. GSMaP_Gauge displays the best error stability and error structure over most basins of China and also exhibits stronger rain-rate dependencies. However, its unexpected positive biases in southeastern basins, which mainly come from the overestimation at lower rain rates, still need to improve further in future developments. We expected the results documented here can both provide the retrieval developers with some valuable references and offer hydrologic users of GSMaP data a better understanding of their error features and potential utilizations for various hydrological applications.

Assessment of small-scale variability of rainfall and multi-satellite precipitation estimates using measurements from a dense rain gauge network in Southeast India

Abstract. This paper describes the establishment of a dense rain gauge network and small-scale variability in rain events (both in space and time) over a complex hilly terrain in Southeast India. Three years of high-resolution gauge measurements are used to validate 3-hourly rainfall and sub-daily variations of four widely used multi-satellite precipitation estimates (MPEs). The network, established as part of the Megha-Tropiques validation program, consists of 36 rain gauges arranged in a near-square grid area of 50 km × 50 km with an intergauge distance of 6–12 km. Morphological features of rainfall in two principal rainy seasons (southwest monsoon, SWM, and northeast monsoon, NEM) show marked differences. The NEM rainfall exhibits significant spatial variability and most of the rainfall is associated with large-scale/long-lived systems (during wet spells), whereas the contribution from small-scale/short-lived systems is considerable during the SWM. Rain events with longer duration and copious rainfall are seen mostly in the western quadrants (a quadrant is 1/4 of the study region) in the SWM and northern quadrants in the NEM, indicating complex spatial variability within the study region. The diurnal cycle also exhibits large spatial and seasonal variability with larger diurnal amplitudes at all the gauge locations (except for 1) during the SWM and smaller and insignificant diurnal amplitudes at many gauge locations during the NEM. On average, the diurnal amplitudes are a factor of 2 larger in the SWM than in the NEM. The 24 h harmonic explains about 70 % of total variance in the SWM and only ∼ 30 % in the NEM. During the SWM, the rainfall peak is observed between 20:00 and 02:00 IST (Indian Standard Time) and is attributed to the propagating systems from the west coast during active monsoon spells. Correlograms with different temporal integrations of rainfall data (1, 3, 12, 24 h) show an increase in the spatial correlation with temporal integration, but the correlation remains nearly the same after 12 h of integration in both monsoon seasons. The 1 h resolution data show the steepest reduction in correlation with intergauge distance and the correlation becomes insignificant after ∼ 30 km in both monsoon seasons. Validation of high-resolution rainfall estimates from various MPEs against the gauge rainfall data indicates that all MPEs underestimate the light and heavy rain. The MPEs exhibit good detection skills of rain at both 3 and 24 h resolutions; however, considerable improvement is observed at 24 h resolution. Among the different MPEs investigated, the Climate Prediction Centre morphing technique (CMORPH) performs better at 3-hourly resolution in both monsoons. The performance of Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA) is much better at daily resolution than at 3-hourly, as evidenced by better statistical metrics than the other MPEs. All MPEs captured the basic shape of the diurnal cycle and the amplitude quite well, but failed to reproduce the weak/insignificant diurnal cycle in the NEM.

Evaluating Four Multisatellite Precipitation Estimates over the Diaoyu Islands during Typhoon Seasons

Abstract The Diaoyu Islands are a group of uninhabited islets located in the East China Sea between Japan, China, and Taiwan. Here, four mainstream gauge-adjusted multisatellite precipitation estimates [TRMM Multisatellite Precipitation Analysis, version 7 (TMPA-V7); CPC morphing technique–bias-corrected product (CMORPH-CRT); Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR); and Global Satellite Mapping of Precipitation–gauge adjusted (GSMaP_Gauge)] are adopted to detect the rainfall characteristics of the Diaoyu Islands area with a particular focus on typhoon contribution. Out of the four products, CMORPH-CRT and GSMaP_Gauge show much more similarity both in terms of the spatial patterns and error structures because of their use of the same morphing technique. Overall, GSMaP_Gauge performs better than the other three products, likely because of denser in situ observations integrated in its retrieval algorithms over East Asia. All rainfall products indicate that an apparent rain belt exists along the northeastern 45° direction of Taiwan extending to Kyushu of Japan, which is physically associated with the Kuroshio. The Diaoyu Islands are located on the central axis of this rain belt. During the period 2001–09, typhoon-induced rainfall accounted for 530 mm yr−1, and typhoons contributed on average approximately 30% of the annual precipitation budget over the Diaoyu Islands. Higher typhoon contribution was found over the southern warmer water of the Diaoyu Islands, while the northern cooler water presented less contribution ratio. Supertyphoon Chaba, the largest typhoon of 2004, recorded 53 h of rainfall accumulation totaling 235 mm on the Diaoyu Islands, and this event caused severe property damage and human casualties for Japan. Hence, the Diaoyu Islands play an important role in weather monitoring and forecasting for the neighboring countries and regions.

Error analysis of multi-satellite precipitation estimates with an independent raingauge observation network over a medium-sized humid basin

ABSTRACTThis study focuses on quantifying the error characteristics of four widely utilized satellite precipitation products (i.e. TMPA 3B42RTV7, TMPA 3B42V7, CMORPH and PERSIANN-CDR) for a five-year period (2005–2009) using an independent raingauge network over the upper-middle Huai River basin in central-eastern China. Assessment results show that CMORPH generally exhibits the best performance with slight underestimation, while 3B42RTV7 has the worst performance with large positive biases. Additionally, 3B42V7 and PERSIANN-CDR tend to have an approximate accuracy. The monthly gauge adjustment applied to 3B42V7 and PERSIANN-CDR significantly reduces their systematic bias and in particular it makes these two research products maintain a stable skill level during winter. As for the heavy rainfall events (>50 mm/d) in summer, 3B42V7 and CMORPH exhibit a relatively better degree of agreement to the gauge observations. Overall, our study suggests that the satellite-based precipitation estimates all have their own pros and cons at different spatiotemporal scales. We expect the results reported here will provide a better understanding of current mainstream satellite precipitation products over similar medium-sized humid basins.Editor Z.W. Kundzewicz; Associate editor N. Verhoest

A preliminary assessment of GPM-based multi-satellite precipitation estimates over a monsoon dominated region

Summary Following the launch of the Global Precipitation Measurement (GPM) Core Observatory, two advanced high resolution multi-satellite precipitation products namely, Integrated Multi-satellitE Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) version 6 are released. A critical evaluation of these newly released precipitation data sets is very important for both the end users and data developers. This study provides a comprehensive assessment of IMERG research product and GSMaP estimates over India at a daily scale for the southwest monsoon season (June to September 2014). The GPM-based precipitation products are inter-compared with widely used TRMM Multi-satellite Precipitation Analysis (TMPA), and gauge-based observations over India. Results show that the IMERG estimates represent the mean monsoon rainfall and its variability more realistically than the gauge-adjusted TMPA and GSMaP data. However, GSMaP has relatively smaller root-mean-square error than IMERG and TMPA, especially over the low mean rainfall regimes and along the west coast of India. An entropy-based approach is employed to evaluate the distributions of the selected precipitation products. The results indicate that the distribution of precipitation in IMERG and GSMaP has been improved markedly, especially for low precipitation rates. IMERG shows a clear improvement in missed and false precipitation bias over India. However, all the three satellite-based rainfall estimates show exceptionally smaller correlation coefficient, larger RMSE, larger negative total bias and hit bias over the northeast India where precipitation is dominated by orographic effects. Similarly, the three satellite-based estimates show larger false precipitation over the southeast peninsular India which is a rain-shadow region. The categorical verification confirms that these satellite-based rainfall estimates have difficulties in detection of rain over the southeast peninsula and northeast India. These preliminary results need to be confirmed in other monsoon seasons in future studies when the fully GPM-based IMERG retrospectively processed data prior to 2014 are available.

Comprehensive evaluation of multisatellite precipitation estimates over India using gridded rainfall data

AbstractThis study presents a comprehensive evaluation of five widely used multisatellite precipitation estimates (MPEs) against 1° × 1° gridded rain gauge data set as ground truth over India. One decade observations are used to assess the performance of various MPEs (Climate Prediction Center (CPC)‐South Asia data set, CPC Morphing Technique (CMORPH), Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks, Tropical Rainfall Measuring Mission's Multisatellite Precipitation Analysis (TMPA‐3B42), and Global Precipitation Climatology Project). All MPEs have high detection skills of rain with larger probability of detection (POD) and smaller “missing” values. However, the detection sensitivity differs from one product (and also one region) to the other. While the CMORPH has the lowest sensitivity of detecting rain, CPC shows highest sensitivity and often overdetects rain, as evidenced by large POD and false alarm ratio and small missing values. All MPEs show higher rain sensitivity over eastern India than western India. These differential sensitivities are found to alter the biases in rain amount differently. All MPEs show similar spatial patterns of seasonal rain bias and root‐mean‐square error, but their spatial variability across India is complex and pronounced. The MPEs overestimate the rainfall over the dry regions (northwest and southeast India) and severely underestimate over mountainous regions (west coast and northeast India), whereas the bias is relatively small over the core monsoon zone. Higher occurrence of virga rain due to subcloud evaporation and possible missing of small‐scale convective events by gauges over the dry regions are the main reasons for the observed overestimation of rain by MPEs. The decomposed components of total bias show that the major part of overestimation is due to false precipitation. The severe underestimation of rain along the west coast is attributed to the predominant occurrence of shallow rain and underestimation of moderate to heavy rain by MPEs. The decomposed components suggest that the missed precipitation and hit bias are the leading error sources for the total bias along the west coast. All evaluation metrics are found to be nearly equal in two contrasting monsoon seasons (southwest and northeast), indicating that the performance of MPEs does not change with the season, at least over southeast India. Among various MPEs, the performance of TMPA is found to be better than others, as it reproduced most of the spatial variability exhibited by the reference.

Evaluation of three high-resolution satellite precipitation estimates: Potential for monsoon monitoring over Pakistan

Multi-sensor precipitation datasets including two products from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and estimates from Climate Prediction Center Morphing Technique (CMORPH) product were quantitatively evaluated to study the monsoon variability over Pakistan. Several statistical and graphical techniques are applied to illustrate the nonconformity of the three satellite products from the gauge observations. During the monsoon season (JAS), the three satellite precipitation products captures the intense precipitation well, all showing high correlation for high rain rates (>30mm/day). The spatial and temporal satellite rainfall error variability shows a significant geo-topography dependent distribution, as all the three products overestimate over mountain ranges in the north and coastal region in the south parts of Indus basin. The TMPA-RT product tends to overestimate light rain rates (approximately 100%) and the bias is low for high rain rates (about ±20%). In general, daily comparisons from 2005 to 2010 show the best agreement between the TMPA-V7 research product and gauge observations with correlation coefficient values ranging from moderate (0.4) to high (0.8) over the spatial domain of Pakistan. The seasonal variation of rainfall frequency has large biases (100–140%) over high latitudes (36N) with complex terrain for daily, monsoon, and pre-monsoon comparisons. Relatively low uncertainties and errors (Bias ±25% and MAE 1–10mm) were associated with the TMPA-RT product during the monsoon-dominated region (32–35N), thus demonstrating their potential use for developing an operational hydrological application of the satellite-based near real-time products in Pakistan for flood monitoring.

국내 수문학적 활용성 평가를 위한 다중위성 강수자료의 검증

다중위성 기반의 강수자료는 국내에서 다양한 수문학적 응용분야에 활용될 수 있다. 본 연구에서는 위성강수의 수문학적 활용성을 평가하기 위하여 TRMM 3B42v6, CMORPH, GSMaP-MVK 등 3가지 다중위성 강수자료를 이용하여 4개강 권역에 대해 2002년부터 2009년까지의 지상관측강수자료와 비교하였다. 분석결과, TRMM 3B42v6은 강수의 발생 빈도와 정량적 크기에서 관측치에 근접한 결과를 보여주었다. CMORPH와 GSMaP-MVK의 경우 TRMM 3B42v6에 비해 상대적으로 관측치에 대한 일치성이 낮고, 과소 추정하는 경향을 보였다. 이러한 다중위성 강수자료와 관측강수량 비교는 4대강 권역에서 모두 일관된 결과를 보여주었다. Multi-satellite precipitation products can provide possible opportunity for diverse hydrological applications in Korea. This study investigated the potential utility of three multi-satellite precipitation estimates, including TRMM(Tropical Rainfall Measuring Mission) 3B42v6, CMORPH(Climate Prediction Center Morphing), and GSMaP(Global Satellite Mapping of Precipitation)-MVK(Moving Vector with Kalman filter), from 2002 to 2009 by comparing with ground-gauged precipitation data over four major river basins of South Korea. The results showed that the frequency and amount of TRMM 3B42v6 estimates agreed well with observations at daily scale. However, CMORPH and GSMaP-MVK exhibited relatively poor performance than TRMM 3B42v6 with significantly underestimated precipitation. Overall, our results were consistent across four major river basins.

고해상도 다중위성 강수자료와 분포형 수문모형의 유출모의 적용

본 연구에서는 다중위성 강수자료의 수문학적 적용성을 평가하기 위하여 Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), Global Satellite Mapping of Precipitation (GSMaP), Climate Prediction Center (CPC) Morphing technique(CMORPH) 등 전 지구 규모의 고해상도 다중위성 강수자료와 분포형 수문모형을 이용하여 유출모의를 수행하였다. 충주댐 유역에 대하여 2002년 1월 1일부터 2009년 12월 31일까지의 기간에 대하여 Coupled Routing and Excess Storage (CREST) 모형을 적용하였다. 분석기간은 준비기간(2002-2003년, 2006-2007년), 보정기간(2004-2005년), 그리고 검증기간(2008-2009년)으로 구분하여 모의를 수행하였다. 각 다중위성 강수자료를 지상관측자료와 비교결과, 강수의 계절적 변동특성은 잘 반영하고 있으나 연강수량합계 및 월평균강수량에서 TMPA는 과대추정을, GSMaP과 CMORPH는 과소추정하는 경향을 보여주었다. 또한 유출분석결과, TMPA를 제외한 GSMaP과 CMORPH의 충주댐 유역에 대한 수문학적 적용성이 매우 낮은 것을 알 수 있었으며, 향후 다중위성 강수자료의 활용에 앞서 통계적 보정이나 강수알고리즘에 대한 개선이 필요한 것으로 판단된다. In this study we evaluated the hydrological applicability of multi-satellite precipitation estimates. Three high-resolution global multi-satellite precipitation products, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), the Global Satellite Mapping of Precipitation (GSMaP), and the Climate Precipitation Center (CPC) Morphing technique (CMORPH), were applied to the Coupled Routing and Excess Storage (CREST) model for the evaluation of their hydrological utility. The CREST model was calibrated from 2002 to 2005 and validated from 2006 to 2009 in the Chungju Dam watershed, including two years of warm-up periods (2002-2003 and 2006-2007). Areal-averaged precipitation time series of the multi-satellite data were compared with those of the ground records. The results indicate that the multi-satellite precipitation can reflect the seasonal variation of precipitation in the Chungju Dam watershed. However, TMPA overestimates the amount of annual and monthly precipitation while GSMaP and CMORPH underestimate the precipitation during the period from 2002 to 2009. These biases of multi-satellite precipitation products induce poor performances in hydrological simulation, although TMPA is better than both of GSMaP and CMORPH. Our results indicate that advanced rainfall algorithms may be required to improve its hydrological applicability in South Korea.

Accounting for Uncertainties of the TRMM Satellite Estimates

Recent advances in the field of remote sensing have led to an increase in available rainfall data on a regional and global scale. Several NASA sponsored satellite missions provide valuable precipitation data. However, the advantages of the data are limited by complications related to the indirect nature of satellite estimates. This study intends to develop a stochastic model for uncertainty analysis of satellite rainfall fields through simulating error fields and imposing them over satellite estimates. In order to examine reliability and performance of the presented model, ensembles of satellite estimates are simulated for a large area across the North and South Carolina. The generated ensembles are then compared with original satellite estimates with respect to statistical properties and spatial dependencies. The results show that the model can be used to describe the uncertainties associated to TRMM multi-satellite precipitation estimates. The presented model is validated using random sub-samples of the observations based on the bootstrap technique. The results indicate that the model performs reasonably well with different numbers of available rain gauges.

Flood and landslide applications of near real-time satellite rainfall products

Floods and associated landslides account for the largest number of natural disasters and affect more people than any other type of natural disaster. With the availability of satellite rainfall analyses at fine time and space resolution, it has also become possible to mitigate such hazards on a near-global basis. In this article, a framework to detect floods and landslides related to heavy rain events in near-real-time is proposed. Key components of the framework are: a fine resolution precipitation acquisition system; a comprehensive land surface database; a hydrological modeling component; and landslide and debris flow model components. A key precipitation input dataset for the integrated applications is the NASA TRMM-based multi-satellite precipitation estimates. This dataset provides near real-time precipitation at a spatial-temporal resolution of 3 h and 0.25° × 0.25°. In combination with global land surface datasets it is now possible to expand regional hazard modeling components into a global identification/monitoring system for flood/landslide disaster preparedness and mitigation.