Impact of TRMM Data on a Low-Latency, High-Resolution Precipitation Algorithm for Flash-Flood Forecasting

Abstract

Data from the Tropical Rainfall Measuring Mission (TRMM) have made great contributions to hydrometeorology from both a science and an operations standpoint. However, direct application of TRMM data to short-fuse hydrologic forecasting has been challenging because of the data refresh and latency issues inherent in an instrument in low Earth orbit (LEO). To evaluate their potential impact on low-latency satellite rainfall estimates, rain rates from both the TRMM Microwave Imager (TMI) and precipitation radar (PR) were ingested into a multisensor framework that calibrates high-refresh, low-latency IR brightness temperature data from geostationary platforms against the more accurate but low-refresh, higher-latency rainfall rates available from microwave (MW) instruments on board LEO platforms. The TRMM data were used in two ways: to bias adjust the other MW data sources to match the distribution of the TMI rain rates, and directly alongside the MW rain rates in the calibration dataset. The results showed a significant reduction in false alarms and also a significant reduction in bias for those pixels for which rainfall was correctly detected. The MW bias adjustment was found to have much greater impact than the direct use of the TMI and PR rain rates in the calibration data, but this is not surprising since the latter represented perhaps only 10% of the calibration dataset.

Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

“…Zulkafli et al [89] conducted a comparative study on the performance of TRMM 3B42 Versions 6 and 7 in streamflow prediction. Kuligowski et al [90] utilized TRMM data for flash flood forecasting. Beside the TRMM precipitation products, the SCaMPR [91] and the CMORPH [92] precipitation products have also been used for operational streamflow/flood forecasting.…”

“…As there are an increasing number of precipitation satellite products available, the first idea is to integrate multiple satellite estimates, which has promoted the growth of multi-satellite precipitation estimation algorithms and related products, such as TMPA and CMORPH mentioned above. Those multi-satellite blended products have been intensively used for hydrologic forcing [81,88,90,92,100]. To make a better use of those QPE products and further constrain the uncertainties, integrated use of multiple multi-satellite QPEs for streamflow simulation has been tested, and improved streamflow simulation has been demonstrated through multiple products ingestion [91] or Bayesian model averaging [57].…”

Incorporating Satellite Precipitation Estimates into a Radar-Gage Multi-Sensor Precipitation Estimation Algorithm

“…SCaMPR is used in SRG-MSF as satellite QPE. SCaMPR rain rates are derived from IR brightness temperatures and derived quantities after being calibrated against microwave rain rates [8,10,11] in an effort to capture the accuracy of microwave rain rates along with the rapid refresh of GOES data.…”

“…Some of the most widely used algorithms retrieved rain rate using (IR) brightness temperature observations from geostationary platforms, which have short latency and wide spatial-temporal coverage, and more accurate estimates from microwave sensors onboard lower-orbit satellites. These include the Climate Prediction C-Morphing (CMORPH) [4,5], TRMM Multi-Sensor Precipitation Analysis (TMPA) and Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM (IMERG) [6,7]; and the Self-Calibrating Multivariate Precipitation Retrieval (SCAMPR) [8][9][10][11]. Recent advances in precipitation retrieval and calibration algorithms have led to substantial improvements to the latency, resolution and accuracy of SQPEs.…”

Incorporating Satellite Precipitation Estimates into a Radar-Gauge Multi-Sensor Precipitation Estimation Algorithm

“…Some of the most widely used algorithms retrieve rain rate using infrared (IR) brightness temperature observations from geostationary platforms, which have short latency and wide spatial-temporal coverage, and more accurate estimates from microwave sensors onboard lower-orbit satellites. These include the Climate Prediction C-Morphing (CMORPH) [4,5], TRMM Multi-Sensor Precipitation Analysis (TMPA) and Global Precipitation Measurement Integrated Multi-satellite Retrievals for GPM (IMERG) [6,7]; and the Self-Calibrating Multivariate Precipitation Retrieval (SCAMPR) [8][9][10][11]. Recent advances in precipitation retrieval and calibration algorithms have led to substantial improvements to the latency, resolution and accuracy of SQPEs.…”