scholarly journals Using ground radar overlaps to verify the retrieval of calibration bias estimates from spaceborne platforms

2020 ◽  
Vol 13 (2) ◽  
pp. 645-659
Author(s):  
Irene Crisologo ◽  
Maik Heistermann
Keyword(s):  
Moving Average ◽  
Weighted Average ◽  
Tropical Rainfall Measuring Mission ◽  
The Philippines ◽  
Weighted Averaging ◽  
Radar Observations ◽  
Precipitation Measurement ◽  
Formal Procedure ◽  
Calibration Techniques ◽  
Global Precipitation

Abstract. Many institutions struggle to tap into the potential of their large archives of radar reflectivity: these data are often affected by miscalibration, yet the bias is typically unknown and temporally volatile. Still, relative calibration techniques can be used to correct the measurements a posteriori. For that purpose, the usage of spaceborne reflectivity observations from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) platforms has become increasingly popular: the calibration bias of a ground radar (GR) is estimated from its average reflectivity difference to the spaceborne radar (SR). Recently, Crisologo et al. (2018) introduced a formal procedure to enhance the reliability of such estimates: each match between SR and GR observations is assigned a quality index, and the calibration bias is inferred as a quality-weighted average of the differences between SR and GR. The relevance of quality was exemplified for the Subic S-band radar in the Philippines, which is greatly affected by partial beam blockage. The present study extends the concept of quality-weighted averaging by accounting for path-integrated attenuation (PIA) in addition to beam blockage. This extension becomes vital for radars that operate at the C or X band. Correspondingly, the study setup includes a C-band radar that substantially overlaps with the S-band radar. Based on the extended quality-weighting approach, we retrieve, for each of the two ground radars, a time series of calibration bias estimates from suitable SR overpasses. As a result of applying these estimates to correct the ground radar observations, the consistency between the ground radars in the region of overlap increased substantially. Furthermore, we investigated if the bias estimates can be interpolated in time, so that ground radar observations can be corrected even in the absence of prompt SR overpasses. We found that a moving average approach was most suitable for that purpose, although limited by the absence of explicit records of radar maintenance operations.

scholarly journals Using ground radar overlaps to verify the retrieval of calibration bias estimates from spaceborne platforms

2019 ◽  
Author(s):  
Irene Crisologo ◽  
Maik Heistermann
Keyword(s):  
Moving Average ◽  
Weighted Average ◽  
Tropical Rainfall Measuring Mission ◽  
The Philippines ◽  
Weighted Averaging ◽  
Radar Observations ◽  
Precipitation Measurement ◽  
Formal Procedure ◽  
Calibration Techniques ◽  
Global Precipitation

Abstract. Many institutions struggle to tap the potential of their large archives of radar reflectivity: these data are often affected by miscalibration, yet the bias is typically unknown and temporally volatile. Still, relative calibration techniques can be used to correct the measurements a posteriori. For that purpose, the usage of spaceborne reflectivity observations from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) platforms has become increasingly popular: the calibration bias of a ground radar is estimated from its average reflectivity difference to the spaceborne radar (SR). Recently, Crisologo et al. (2018) introduced a formal procedure to enhance the reliability of such estimates: each match between SR and GR observations is assigned a quality index, and the calibration bias is inferred as a quality-weighted average of the differences between SR and GR. The relevance of quality was exemplified for the Subic S-band radar in the Philippines which is much affected by partial beam blockage. The present study extends the concept of quality-weighted averaging by accounting for path-integrated attenuation (PIA), in addition to beam blockage. This extension becomes vital for radars that operate at C- or X-band. Correspondingly, the study setup includes a C-band radar which substantially overlaps with the S-band radar. Based on the extended quality-weighting approach, we retrieve, for each of the two ground radars, a time series of calibration bias estimates from suitable SR overpasses. As a result of applying these estimates to correct the ground radar observations, the consistency between the ground radars in the region of overlap increased substantially. Furthermore, we investigated if the bias estimates can be interpolated in time, so that ground radar observations can be corrected even in the absence of prompt SR overpasses. We found that a moving average approach was most suitable for that purpose, although limited by the absence of explicit records of radar maintenance operations.

scholarly journals Enhancing the consistency of spaceborne and ground-based radar comparisons by using beam blockage fraction as a quality filter

2018 ◽  
Vol 11 (9) ◽  
pp. 5223-5236 ◽  
Author(s):  
Irene Crisologo ◽  
Robert A. Warren ◽  
Kai Mühlbauer ◽  
Maik Heistermann
Keyword(s):  
Quality Index ◽  
Weighted Average ◽  
Tropical Rainfall Measuring Mission ◽  
Radar Data ◽  
Weighted Averaging ◽  
Precipitation Measurement ◽  
Radar Calibration ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Spaceborne Radar

Abstract. We explore the potential of spaceborne radar (SR) observations from the Ku-band precipitation radars onboard the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) satellites as a reference to quantify the ground radar (GR) reflectivity bias. To this end, the 3-D volume-matching algorithm proposed by Schwaller and Morris (2011) is implemented and applied to 5 years (2012–2016) of observations. We further extend the procedure by a framework to take into account the data quality of each ground radar bin. Through these methods, we are able to assign a quality index to each matching SR–GR volume, and thus compute the GR calibration bias as a quality-weighted average of reflectivity differences in any sample of matching GR–SR volumes. We exemplify the idea of quality-weighted averaging by using the beam blockage fraction as the basis of a quality index. As a result, we can increase the consistency of SR and GR observations, and thus the precision of calibration bias estimates. The remaining scatter between GR and SR reflectivity as well as the variability of bias estimates between overpass events indicate, however, that other error sources are not yet fully addressed. Still, our study provides a framework to introduce any other quality variables that are considered relevant in a specific context. The code that implements our analysis is based on the wradlib open-source software library, and is, together with the data, publicly available to monitor radar calibration or to scrutinize long series of archived radar data back to December 1997, when TRMM became operational.

scholarly journals Validation of Satellite-Based Precipitation Products from TRMM to GPM

2021 ◽  
Vol 13 (9) ◽  
pp. 1745
Author(s):  
Jianxin Wang ◽  
Walter A. Petersen ◽  
David B. Wolff
Keyword(s):  
Regional Differences ◽  
Tropical Rainfall Measuring Mission ◽  
Decomposition Approach ◽  
Precipitation Product ◽  
Precipitation Measurement ◽  
Global Precipitation Measurement ◽  
Error Decomposition ◽  
Improved Accuracy ◽  
Global Precipitation ◽  
Statistical Metrics

The global precipitation measurement mission (GPM) has been in operation for seven years and continues to provide a vast quantity of global precipitation data at finer temporospatial resolutions with improved accuracy and coverage. GPM’s signature algorithm, the integrated multisatellite retrievals for GPM (IMERG) is a next-generation of precipitation product expected for wide variety of research and operational applications. This study evaluates the latest version (V06B) of IMERG and its predecessor, the tropical rainfall measuring mission (TRMM) multisatellite precipitation (TMPA) 3B42 (V7) using ground-based and gauge-corrected multiradar multisensor system (MRMS) precipitation products over the conterminous United States (CONUS). The spatial distributions of all products are analyzed. The error characteristics are further examined for 3B42 and IMERG in winter and summer by an error decomposition approach, which partitions total bias into hit bias, biases due to missed precipitation and false precipitation. The volumetric and categorical statistical metrics are used to quantitatively evaluate the performance of the two satellite-based products. All products show a similar precipitation climatology with some regional differences. The two satellite-based products perform better in the eastern CONUS than in the mountainous Western CONUS. The evaluation demonstrates the clear improvement in IMERG precipitation product in comparison with its predecessor 3B42, especially in reducing missed precipitation in winter and summer, and hit bias in winter, resulting in better performance in capturing lighter and heavier precipitation.

Combined Radar and Radiometer Analysis of Precipitation Profiles for a Parametric Retrieval Algorithm

2005 ◽  
Vol 22 (7) ◽  
pp. 909-929 ◽  
Author(s):  
Hirohiko Masunaga ◽  
Christian D. Kummerow
Keyword(s):  
A Priori ◽  
Three Dimensional ◽  
Tropical Rainfall Measuring Mission ◽  
Dimensional Structure ◽  
Retrieval Algorithm ◽  
Brightness Temperatures ◽  
Precipitation Measurement ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Global Precipitation

Abstract A methodology to analyze precipitation profiles using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) is proposed. Rainfall profiles are retrieved from PR measurements, defined as the best-fit solution selected from precalculated profiles by cloud-resolving models (CRMs), under explicitly defined assumptions of drop size distribution (DSD) and ice hydrometeor models. The PR path-integrated attenuation (PIA), where available, is further used to adjust DSD in a manner that is similar to the PR operational algorithm. Combined with the TMI-retrieved nonraining geophysical parameters, the three-dimensional structure of the geophysical parameters is obtained across the satellite-observed domains. Microwave brightness temperatures are then computed for a comparison with TMI observations to examine if the radar-retrieved rainfall is consistent in the radiometric measurement space. The inconsistency in microwave brightness temperatures is reduced by iterating the retrieval procedure with updated assumptions of the DSD and ice-density models. The proposed methodology is expected to refine the a priori rain profile database and error models for use by parametric passive microwave algorithms, aimed at the Global Precipitation Measurement (GPM) mission, as well as a future TRMM algorithms.

scholarly journals Systematical Evaluation of GPM IMERG and TRMM 3B42V7 Precipitation Products in the Huang-Huai-Hai Plain, China

2019 ◽  
Vol 11 (6) ◽  
pp. 697 ◽  
Author(s):  
Fenglin Xu ◽  
Bin Guo ◽  
Bei Ye ◽  
Qia Ye ◽  
Huining Chen ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Accurate Estimation ◽  
Detection Capability ◽  
Satellite Precipitation ◽  
Precipitation Measurement ◽  
Daily Precipitation Data ◽  
Precipitation Estimates ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Precipitation Events

Accurate estimation of high-resolution satellite precipitation products like Global Precipitation Measurement (GPM) and Tropical Rainfall Measuring Mission (TRMM) is critical for hydrological and meteorological research, providing a benchmark for the continued development and future improvement of these products. This study aims to comprehensively evaluate the Integrated Multi-Satellite Retrievals for GPM (IMERG) and TRMM 3B42V7 products at multiple temporal scales from 1 January 2015 to 31 December 2017 over the Huang-Huai-Hai Plain in China, using daily precipitation data from 59 meteorological stations. Three commonly used statistical metrics (CC, RB, and RMSE) are adopted to quantitatively verify the accuracy of two satellite precipitation products. The assessment also takes into account the precipitation detection capability (POD, FAR, CSI, and ACC) and frequency of different precipitation intensities. The results show that the IMERG and 3B42V7 present strong correlation with meteorological stations observations at annual and monthly scales (CC > 0.90), whereas moderate at the daily scale (CC = 0.76 and 0.69 for IMERG and 3B42V7, respectively). The spatial variability of the annual and seasonal precipitation is well captured by these two satellite products. And spatial patterns of precipitation gradually decrease from south to north over the Huang-Huai-Hai Plain. Both IMERG and 3B42V7 products overestimate precipitation compared with the station observations, of which 3B42V7 has a lower degree of overestimation. Relative to the IMERG, annual precipitation estimates from 3B42V7 show lower RMSE (118.96 mm and 142.67 mm, respectively), but opposite at the daily, monthly, and seasonal scales. IMERG has a better precipitation detection capability than 3B42V7 (POD = 0.83 and 0.67, respectively), especially when detecting trace and solid precipitation. The two precipitation products tend to overestimate moderate (2–10 mm/d) and heavy (10–50 mm/d) precipitation events, but underestimate violent (>50 mm/d) precipitation events. The IMERG is not found capable to detecting precipitation events of different frequencies more precisely. In general, the accuracy of IMERG is better than 3B42V7 product in the Huang-Huai-Hai Plain. The IMERG satellite precipitation product with higher temporal and spatial resolutions can be regarded a reliable data sources in studying hydrological and climatic research.

scholarly journals Assessment of IMERG-V06 Precipitation Product over Different Hydro-Climatic Regimes in the Tianshan Mountains, North-Western China

2019 ◽  
Vol 11 (19) ◽  
pp. 2314 ◽  
Author(s):  
Anjum ◽  
Ahmad ◽  
Ding ◽  
Shangguan ◽  
Zaman ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Tianshan Mountains ◽  
Western China ◽  
Spatiotemporal Variability ◽  
Eastern Region ◽  
Monthly Precipitation ◽  
Precipitation Measurement ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Precipitation Events

This study presents an assessment of the version-6 (V06) of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) product from June 2014 to December 2017 over different hydro-climatic regimes in the Tianshan Mountains. The performance of IMERG-V06 was compared with IMERG-V05 and the Tropical Rainfall Measuring Mission (TRMM) 3B42V7 precipitation products. The precipitation products were assessed against gauge-based daily and monthly precipitation observations over the entire spatial domain and five hydro-climatologically distinct sub-regions. Results showed that: (1) The spatiotemporal variability of average daily precipitation over the study domain was well represented by all products. (2) All products showed better correlations with the monthly gauge-based observations than the daily data. Compared to 3B42V7, both IMERG products presented a better agreement with gauge-based observations. (3) The estimation skills of all precipitation products showed significant spatial variations. Overall performance of all precipitation products was better in the Eastern region compared to the Middle and Western regions. (4) Satellite products were able to detect tiny precipitation events, but they were uncertain in capturing light and moderate precipitation events. (5) No significant improvements in the precipitation estimation skill of IMERG-V06 were found as compared to IMERG-V05. We deduce that the IMERG-V06 precipitation detection capability could not outperform the efficiency of IMERG-V05. This comparative evaluation of the research products of Global Precipitation Measurement (GPM) and TRMM products in the Tianshan Mountains is useful for data users and algorithm developers.

scholarly journals The role of geomorphology, rainfall and soil moisture in the occurrence of landslides triggered by 2018 Typhoon Mangkhut in the Philippines

2021 ◽  
Vol 21 (5) ◽  
pp. 1531-1550
Author(s):  
Clàudia Abancó ◽  
Georgina L. Bennett ◽  
Adrian J. Matthews ◽  
Mark Anthony M. Matera ◽  
Fibor J. Tan
Keyword(s):  
Soil Moisture ◽  
High Intensity ◽  
The Philippines ◽  
Saturation Point ◽  
Rainfall Events ◽  
Precipitation Measurement ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
First Time

Abstract. In 2018 Typhoon Mangkhut (locally known as Typhoon Ompong) triggered thousands of landslides in the Itogon region of the Philippines. A landslide inventory of the affected region is compiled for the first time, comprising 1101 landslides over a 570 km2 area. The inventory is used to study the geomorphological characteristics and land cover more prone to landsliding as well as the hydrometeorological conditions that led to widespread failure. The results showed that landslides mostly occurred on grassland and wooded slopes of clay superficial geology, predominantly facing east-southeast. Rainfall (Integrated Multi-satellitE Retrievals for Global Precipitation Measurement, IMERG GPM) associated with Typhoon Mangkhut is compared with 33 high-intensity rainfall events that did not trigger regional landslide events in 2018. Results show that landslides occurred during high-intensity rainfall that coincided with the highest soil moisture values (estimated clays saturation point), according to Soil Moisture Active Passive level 4 (SMAP-L4) data. Our results demonstrate the potential of SMAP-L4 and GPM IMERG data for landslide hazard assessment and early warning where ground-based data are scarce. However, other rainfall events in the months leading up to Typhoon Mangkhut that had similar or higher rainfall intensities and also occurred when soils were saturated did not trigger widespread landsliding, highlighting the need for further research into the conditions that trigger landslides in typhoons.

scholarly journals NASA’s Remotely Sensed Precipitation: A Reservoir for Applications Users

2017 ◽  
Vol 98 (6) ◽  
pp. 1169-1184 ◽  
Author(s):  
Dalia B. Kirschbaum ◽  
George J. Huffman ◽  
Robert F. Adler ◽  
Scott Braun ◽  
Kevin Garrett ◽  
...  
Keyword(s):  
Water Cycle ◽  
Earth Science ◽  
Weather Prediction ◽  
Tropical Rainfall Measuring Mission ◽  
Public Health Research ◽  
End User ◽  
Science Data ◽  
Precipitation Measurement ◽  
Earth Science Data ◽  
Global Precipitation

Abstract Precipitation is the fundamental source of freshwater in the water cycle. It is critical for everyone, from subsistence farmers in Africa to weather forecasters around the world, to know when, where, and how much rain and snow is falling. The Global Precipitation Measurement (GPM) Core Observatory spacecraft, launched in February 2014, has the most advanced instruments to measure precipitation from space and, together with other satellite information, provides high-quality merged data on rain and snow worldwide every 30 min. Data from GPM and the predecessor Tropical Rainfall Measuring Mission (TRMM) have been fundamental to a broad range of applications and end-user groups and are among the most widely downloaded Earth science data products across NASA. End-user applications have rapidly become an integral component in translating satellite data into actionable information and knowledge used to inform policy and enhance decision-making at local to global scales. In this article, we present NASA precipitation data, capabilities, and opportunities from the perspective of end users. We outline some key examples of how TRMM and GPM data are being applied across a broad range of sectors, including numerical weather prediction, disaster modeling, agricultural monitoring, and public health research. This work provides a discussion of some of the current needs of the community as well as future plans to better support end-user communities across the globe to utilize this data for their own applications.

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