scholarly journals Validation of Satellite-Based High-Resolution Rainfall Products over the Korean Peninsula Using Data from a Dense Rain Gauge Network

2010 ◽  
Vol 49 (4) ◽  
pp. 701-714 ◽  
Author(s):  
B. J. Sohn ◽  
Hyo-Jin Han ◽  
Eun-Kyoung Seo
Keyword(s):  
High Resolution ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Diurnal Cycles ◽  
South Korean ◽  
Precipitation Estimation ◽  
Surface Measurements ◽  
Rain Events ◽  
Using Data ◽  
June July

Abstract Four independently developed high-resolution precipitation products [HRPPs; the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), the Climate Prediction Center Morphing Method (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the National Research Laboratory (NRL) blended precipitation dataset (NRL-blended)], with a spatial resolution of 0.25° and a temporal resolution of 3 h, were compared with surface rain measurements for the four summer seasons (June, July, and August) from 2003 to 2006. Surface measurements are 1-min rain gauge data from the Automated Weather Station (AWS) network operated by the Korean Meteorological Administration (KMA) over South Korea, which consists of about 520 sites. The summer mean rainfall and diurnal cycles of TMPA are comparable to those of the AWS, but with larger magnitudes. The closer agreement of TMPA with surface observations is due to the adjustment of the real-time version of TMPA products to monthly gauge measurements. However, the adjustment seems to result in significant overestimates for light or moderate rain events and thus increased RMS error. In the other three products (CMORPH, PERSIANN, and NRL-blended), significant underestimates are evident in the summer mean distribution and in scatterplots for the grid-by-grid comparison. The magnitudes of the diurnal cycles of the three products appear to be much smaller than those suggested by AWS, although CMORPH shows nearly the same diurnal phase as in AWS. Such underestimates by three methods are likely due to the deficiency of the passive microwave (PMW)-based rainfall retrievals over the South Korean region. More accurate PMW measurements (in particular by the improved land algorithm) seem to be a prerequisite for better estimates of the rain rate by HRPP algorithms. This paper further demonstrates the capability of the Korean AWS network data for validating satellite-based rain products.

scholarly journals Evaluation of Three Satellite Precipitation Products TRMM 3B42, CMORPH, and PERSIANN over a Subtropical Watershed in China

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Junzhi Liu ◽  
Zheng Duan ◽  
Jingchao Jiang ◽  
A-Xing Zhu
Keyword(s):  
Comprehensive Evaluation ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Lake Basin ◽  
Satellite Precipitation ◽  
Precipitation Estimation ◽  
Daily Scale ◽  
Using Data ◽  
Hydrological Applications ◽  
Trmm 3B42

This study conducted a comprehensive evaluation of three satellite precipitation products (TRMM (Tropical Rainfall Measuring Mission) 3B42, CMORPH (the Climate Prediction Center (CPC) Morphing algorithm), and PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks)) using data from 52 rain gauge stations over the Meichuan watershed, which is a representative watershed of the Poyang Lake Basin in China. All the three products were compared and evaluated during a 9-year period at different spatial (grid and watershed) and temporal (daily, monthly, and annual) scales. The results showed that at daily scale, CMORPH had the best performance with coefficients of determination (R2) of 0.61 at grid scale and 0.74 at watershed scale. For precipitation intensities larger than or equal to 25 mm, RMSE% of CMORPH and TRMM 3B42 were less than 50%, indicating CMORPH and TRMM 3B42 might be useful for hydrological applications at daily scale. At monthly and annual temporal scales, TRMM 3B42 had the best performances, with highR2ranging from 0.93 to 0.99, and thus was deemed to be reliable and had good potential for hydrological applications at monthly and annual scales. PERSIANN had the worst performance among the three products at all cases.

scholarly journals Evaluation of high-resolution satellite precipitation products using rain gauge observations over the Tibetan Plateau

2012 ◽  
Vol 9 (8) ◽  
pp. 9503-9532 ◽  
Author(s):  
Y. C. Gao ◽  
M. F. Liu
Keyword(s):  
High Resolution ◽  
Tibetan Plateau ◽  
Correlation Coefficients ◽  
Tropical Rainfall Measuring Mission ◽  
Global Precipitation Climatology Project ◽  
Rain Gauge ◽  
The Tibetan Plateau ◽  
Satellite Precipitation ◽  
Precipitation Estimation ◽  
Mean Square Errors

Abstract. High-resolution satellite precipitation products are very attractive for studying the hydrologic processes in mountainous areas where rain gauges are generally sparse. Three high-resolution satellite precipitation products are evaluated using gauge measurements over different climate zones of the Tibetan Plateau (TP) within a 6 yr period from 2004 to 2009. The three satellite-based precipitation datasets are: Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), Climate Prediction Center Morphing Technique (CMOPRH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN). TMPA and CMORPH, with higher correlation coefficients and lower root mean square errors (RMSEs), show overall better performance than PERSIANN. TMPA has the lowest biases among the three precipitation datasets, which is likely due to the correction process against monthly gauge observations from global precipitation climatology project (GPCP). The three products show better agreement with gauge measurements over humid regions than that over arid regions where correlation coefficients are less than 0.5. Moreover, the three precipitation products generally tend to overestimate light rainfall (0–10 mm) and underestimate moderate and heavy rainfall (>10 mm). PERSIANN produces obvious underestimation at low elevations and overestimation at high elevations. CMORPH and TMPA do not present strong bias-elevation relationships in most regions of TP.

scholarly journals Error Analysis of Satellite Precipitation Products in Mountainous Basins

2014 ◽  
Vol 15 (5) ◽  
pp. 1778-1793 ◽  
Author(s):  
Yiwen Mei ◽  
Emmanouil N. Anagnostou ◽  
Efthymios I. Nikolopoulos ◽  
Marco Borga
Keyword(s):  
Error Analysis ◽  
Warm Season ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Precipitation ◽  
Rain Gauge ◽  
Satellite Precipitation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Regimes ◽  
Precipitation Estimation ◽  
High Precipitation

Abstract Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards such as flash floods, shallow landslides, and debris flows, triggered by heavy precipitation events (HPEs). In situ observations over mountainous areas are limited, but currently available satellite precipitation products can potentially provide the precipitation estimation needed for hydrological applications. In this study, four widely used satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42, version 7 (3B42-V7), and in near–real time (3B42-RT); Climate Prediction Center (CPC) morphing technique (CMORPH); and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)] are evaluated with respect to their performance in capturing the properties of HPEs over different basin scales. Evaluation is carried out over the upper Adige River basin (eastern Italian Alps) for an 8-yr period (2003–10). Basin-averaged rainfall derived from a dense rain gauge network in the region is used as a reference. Satellite precipitation error analysis is performed for warm (May–August) and cold (September–December) season months as well as for different quantile ranges of basin-averaged precipitation accumulations. Three error metrics and a score system are introduced to quantify the performances of the various satellite products. Overall, no single precipitation product can be considered ideal for detecting and quantifying HPE. Results show better consistency between gauges and the two 3B42 products, particularly during warm season months that are associated with high-intensity convective events. All satellite products are shown to have a magnitude-dependent error ranging from overestimation at low precipitation regimes to underestimation at high precipitation accumulations; this effect is more pronounced in the CMORPH and PERSIANN products.

Evolving Multisensor Precipitation Estimation Methods: Their Impacts on Flow Prediction Using a Distributed Hydrologic Model

2011 ◽  
Vol 12 (6) ◽  
pp. 1414-1431 ◽  
Author(s):  
David Kitzmiller ◽  
Suzanne Van Cooten ◽  
Feng Ding ◽  
Kenneth Howard ◽  
Carrie Langston ◽  
...  
Keyword(s):  
High Resolution ◽  
Positive Impact ◽  
Radar Data ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Estimation Methods ◽  
Freezing Level ◽  
Distributed Hydrologic Model ◽  
Precipitation Estimation ◽  
Precipitation Estimates

Abstract This study investigates evolving methodologies for radar and merged gauge–radar quantitative precipitation estimation (QPE) to determine their influence on the flow predictions of a distributed hydrologic model. These methods include the National Mosaic and QPE algorithm package (NMQ), under development at the National Severe Storms Laboratory (NSSL), and the Multisensor Precipitation Estimator (MPE) and High-Resolution Precipitation Estimator (HPE) suites currently operational at National Weather Service (NWS) field offices. The goal of the study is to determine which combination of algorithm features offers the greatest benefit toward operational hydrologic forecasting. These features include automated radar quality control, automated Z–R selection, brightband identification, bias correction, multiple radar data compositing, and gauge–radar merging, which all differ between NMQ and MPE–HPE. To examine the spatial and temporal characteristics of the precipitation fields produced by each of the QPE methodologies, high-resolution (4 km and hourly) gridded precipitation estimates were derived by each algorithm suite for three major precipitation events between 2003 and 2006 over subcatchments within the Tar–Pamlico River basin of North Carolina. The results indicate that the NMQ radar-only algorithm suite consistently yielded closer agreement with reference rain gauge reports than the corresponding HPE radar-only estimates did. Similarly, the NMQ radar-only QPE input generally yielded hydrologic simulations that were closer to observations at multiple stream gauging points. These findings indicate that the combination of Z–R selection and freezing-level identification algorithms within NMQ, but not incorporated within MPE and HPE, would have an appreciable positive impact on hydrologic simulations. There were relatively small differences between NMQ and HPE gauge–radar estimates in terms of accuracy and impacts on hydrologic simulations, most likely due to the large influence of the input rain gauge information.

Evaluation of High-Resolution Satellite Precipitation Products over Very Complex Terrain in Ethiopia

2010 ◽  
Vol 49 (5) ◽  
pp. 1044-1051 ◽  
Author(s):  
Feyera A. Hirpa ◽  
Mekonnen Gebremichael ◽  
Thomas Hopson
Keyword(s):  
Neural Networks ◽  
Complex Terrain ◽  
Tropical Rainfall Measuring Mission ◽  
High Elevation ◽  
Rain Gauge ◽  
Satellite Precipitation ◽  
Precipitation Estimation ◽  
Precipitation Analysis ◽  
Climate Prediction Center ◽  
Microwave Data

Abstract This study focuses on the evaluation of 3-hourly, 0.25° × 0.25°, satellite-based precipitation products: the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT, the NOAA/Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). CMORPH is primarily microwave based, 3B42RT is primarily microwave based when microwave data are available and infrared based when microwave data are not available, and PERSIANN is primarily infrared based. The results show that 1) 3B42RT and CMORPH give similar rainfall fields (in terms of bias, spatial structure, elevation-dependent trend, and distribution function), which are different from PERSIANN rainfall fields; 2) PERSIANN does not show the elevation-dependent trend observed in rain gauge values, 3B42RT, and CMORPH; and 3) PERSIANN considerably underestimates rainfall in high-elevation areas.

Rainfall Contributions from Precipitation Systems with Different Sizes, Convective Intensities, and Durations over the Tropics and Subtropics

2011 ◽  
Vol 12 (3) ◽  
pp. 394-412 ◽  
Author(s):  
Chuntao Liu
Keyword(s):  
Grid Point ◽  
Tropical Rainfall Measuring Mission ◽  
Life Cycles ◽  
Diurnal Variations ◽  
Diurnal Cycles ◽  
Brightness Temperatures ◽  
Seasonal And Diurnal Variations ◽  
Microwave Imager ◽  
Rain Events ◽  
The Tropics

Abstract The rainfall contributions from precipitation features (PFs) with full spectra of different sizes and convective intensities over the tropics and subtropics are summarized using 12 yr of version 6 Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and Microwave Imager (TMI) observations. Regional, seasonal, and diurnal variations of the rainfall contributions from various PFs are shown, with the global distribution of the sizes, PR echo tops, maximum heights of 30 dBZ, and minimum TMI 85-GHz brightness temperatures of PFs above which contribute half of the rainfall in each 2° × 2° region. Though the results from radar and microwave observations generally agree with each other, some large differences exist over land. Seasonal variations of sizes and intensities of precipitation systems are found over the northeast Pacific, northern SPCZ, and some land areas in addition to the well-known monsoon regions. The diurnal cycles of rainfall over land and ocean are interpreted with the combinations of life cycles of various precipitation systems, using the diurnal variations of rainfall contributions from precipitation systems with different sizes and intensities. The long-duration rainfall events with more than four consecutive 3-h periods with rain at a grid point are identified from 11 yr of TRMM 3B42 products. These “12-h rain events” contribute a larger proportion of the total rainfall over ocean than over land. They are mostly correlated with precipitation systems with large sizes and intense convection. However, they can also be caused by some shallow persistent precipitation systems, such as those over the northeast slope of the Andes in Peru in spring and fall and over the west coast of India in summer.

Correction of Radar QPE Errors for Non-Uniform VPRs Using TRMM Observations

2013 ◽  
Vol 726-731 ◽  
pp. 3391-3396
Author(s):  
Man Zhang ◽  
You Cun Qi
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Tropical Rainfall Measuring Mission ◽  
Cool Season ◽  
Convective Systems ◽  
Freezing Level ◽  
Stratiform Region ◽  
Stratiform Precipitation ◽  
Precipitation Estimation ◽  
Mesoscale Convective

Mesoscale Convective Systems (MCSs) contain both regions of convective and stratiform precipitation, and a bright band (BB) or equivalent high-reflectivity region is often found in the stratiform precipitation. Inflated reflectivity intensities in the BB often cause positive biases in radar quantitative precipitation estimation (QPE), and a vertical profile of reflectivity (VPR) correction is necessary to reduce the error. VPR corrections of the radar QPE is more difficult for MCSs than for a widespread cool season stratiform precipitation because of the spatial non-homogeneity of MCSs. Further, microphysical processes in the MCS stratiform region are more complicated than in the large-scale cool season stratiform precipitation. A clearly defined BB bottom, which is critical for accurate VPR corrections, is often not found in ground radar VPRs from MCSs. This is a big challenge when the stratiform region of MCSs is far away from the radar where the radar beam is too high or too wide to resolve the BB bottom. Further, variations of reflectivity below the freezing level are much more significant in MCSs than in a large-scale cool season precipitation, requiring high-resolution radar observations near the ground for an effective VPR correction. The current study seeks to use the vertical precipitation structure observed from Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR) to aid VPR corrections of the ground radar QPE in MCSs. High-resolution VPRs are derived from TRMM data for MCSs and then applied for the correction of ground radar QPEs.

scholarly journals A Review of Merged High-Resolution Satellite Precipitation Product Accuracy during the Tropical Rainfall Measuring Mission (TRMM) Era

2016 ◽  
Vol 17 (4) ◽  
pp. 1101-1117 ◽  
Author(s):  
Viviana Maggioni ◽  
Patrick C. Meyers ◽  
Monique D. Robinson
Keyword(s):  
Real Time ◽  
Motion Vector ◽  
Tropical Rainfall Measuring Mission ◽  
Probability Of Detection ◽  
Satellite Precipitation ◽  
Tropical Rainfall ◽  
Precipitation Estimation ◽  
Rain Events ◽  
Climate Studies ◽  
Liquid Precipitation

Abstract A great deal of expertise in satellite precipitation estimation has been developed during the Tropical Rainfall Measuring Mission (TRMM) era (1998–2015). The quantification of errors associated with satellite precipitation products (SPPs) is crucial for a correct use of these datasets in hydrological applications, climate studies, and water resources management. This study presents a review of previous work that focused on validating SPPs for liquid precipitation during the TRMM era through comparisons with surface observations, both in terms of mean errors and detection capabilities across different regions of the world. Several SPPs have been considered: TMPA 3B42 (research and real-time products), CPC morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP; both the near-real-time and the Motion Vector Kalman filter products), PERSIANN, and PERSIANN–Cloud Classification System (PERSIANN-CCS). Topography, seasonality, and climatology were shown to play a role in the SPP’s performance, especially in terms of detection probability and bias. Regions with complex terrain exhibited poor rain detection and magnitude-dependent mean errors; low probability of detection was reported in semiarid areas. Winter seasons, usually associated with lighter rain events, snow, and mixed-phase precipitation, showed larger biases.

scholarly journals Uncertainty Assessments of Satellite Derived Rainfall Products

2016 ◽  
Author(s):  
Margaret Kimani ◽  
Joost Hoedjes ◽  
Zhongbo Su
Keyword(s):  
Large Scale ◽  
Rainfall Variability ◽  
Tropical Rainfall Measuring Mission ◽  
Global Precipitation Climatology Project ◽  
Rain Gauge ◽  
Climate Data ◽  
Error Distributions ◽  
Precipitation Estimation ◽  
Scatter Plots ◽  
Global Precipitation

Accurate and consistent rainfall observations are vital for climatological studies in support of better planning and decision making. However, estimation of accurate spatial rainfall is limited by sparse rain gauge distributions. Satellite rainfall products can thus potentially play a role in spatial rainfall estimation but their skill and uncertainties need to be under-stood across spatial-time scales. This study aimed at assessing the temporal and spatial performance of seven satellite products (TARCAT (Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) African Rainfall Climatology And Time series), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Tropical Rainfall Measuring Mission (TRMM-3B43), Climate Prediction Center (CPC) Morphing (CMORPH), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Climate Data Record (PERSIANN-CDR), CPC Merged Analysis of Precipitation (CMAP) and Global Precipitation Climatology Project (GPCP) using gridded (0.05o) rainfall data over East Africa for 15 years(1998-2012). The products’ error distributions were qualitatively compared with large scale horizontal winds (850 mb) and elevation patterns with respect to corresponding rain gauge data for each month during the ‘long’ (March-May) and ‘short’ (October-December) rainfall seasons. For validation only rainfall means extracted from 284 rain gauge stations were used, from which qualitative analysis using continuous statistics of Root Mean Squared Difference, Standard deviations, Correlations, coefficient of determinations (from scatter plots) were used to evaluate the products’ performance. Results revealed rainfall variability dependence on wind flows and modulated by topographic influences. The products’ errors showed seasonality and dependent on rainfall intensity and topography. Single sensor and coarse resolution products showed lowest performance on high ground areas. All the products showed low skills in retrieving rainfall during ‘short’ rainfall season when orographic processes were dominant. CHIRPS, CMORPH and TRMM performed well, with TRMM showing the best performance in both seasons. There is need to reduce products’ errors before applications.

scholarly journals A TRMM-Calibrated infrared technique for rainfall estimation: application on rain events over eastern Mediterranean

2006 ◽  
Vol 7 ◽  
pp. 181-188 ◽  
Author(s):  
H. Feidas ◽  
G. Kokolatos ◽  
A. Negri ◽  
M. Manyin ◽  
N. Chrysoulakis
Keyword(s):  
Eastern Mediterranean ◽  
Regional Scale ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Tropical Areas ◽  
Trmm Precipitation ◽  
Remote Sensing Techniques ◽  
Rain Events ◽  
Rain Rates ◽  
Rain Gauge Network

Abstract. The aim is to evaluate the use of a satellite infrared (IR) technique for estimating rainfall over the eastern Mediterranean. The Convective-Stratiform Technique (CST), calibrated by coincident, physically retrieved rain rates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), is applied over the Eastern Mediterranean for four rain events during the six month period of October 2004 to March 2005. Estimates from this technique are verified over a rain gauge network for different time scales. Results show that PR observations can be applied to improve IR-based techniques significantly in the conditions of a regional scale area by selecting adequate calibration areas and periods. They reveal, however, the limitations of infrared remote sensing techniques, originally developed for tropical areas, when applied to precipitation retrievals in mid-latitudes.

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