scholarly journals A Neural Networks–Based Fusion Technique to Estimate Half-Hourly Rainfall Estimates at 0.1° Resolution from Satellite Passive Microwave and Infrared Data

2004 ◽  
Vol 43 (4) ◽  
pp. 576-594 ◽  
Author(s):  
Francisco J. Tapiador ◽  
Chris Kidd ◽  
Vincenzo Levizzani ◽  
Frank S. Marzano
Keyword(s):  
Neural Networks ◽  
Passive Microwave ◽  
Fusion Technique ◽  
Hourly Rainfall ◽  
Rainfall Estimates ◽  
Infrared Data

scholarly journals Susceptibility mapping and estimation of rainfall threshold using space based input for assessment of landslide hazard in Guwahati city in North East India

2014 ◽  
Vol XL-8 ◽  
pp. 15-19 ◽  
Author(s):  
K. Bhusan ◽  
S. S. Kundu ◽  
K. Goswami ◽  
S. Sudhakar
Keyword(s):  
High Resolution ◽  
Rainfall Threshold ◽  
Landslide Hazard ◽  
Capital City ◽  
Landslide Risk ◽  
Eastern Region ◽  
North East ◽  
Hourly Rainfall ◽  
Guwahati City ◽  
Rainfall Estimates

Slopes are the most common landforms in North Eastern Region (NER) of India and because of its relatively immature topography, active tectonics, and intense rainfall activities; the region is susceptible to landslide incidences. The scenario is further aggravated due to unscientific human activities leading to destabilization of slopes. Guwahati, the capital city of Assam also experiences similar hazardous situation especially during monsoon season thus demanding a systematic study towards landslide risk reduction. A systematic assessment of landslide hazard requires understanding of two components, "where" and "when" that landslides may occur. Presently no such system exists for Guwahati city due to lack of landslide inventory data, high resolution thematic maps, DEM, sparse rain gauge network, etc. The present study elucidates the potential of space-based inputs in addressing the problem in absence of field-based observing networks. First, Landslide susceptibility map in 1 : 10,000 scale was derived by integrating geospatial datasets interpreted from high resolution satellite data. Secondly, the rainfall threshold for dynamic triggering of landslide was estimated using rainfall estimates from Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis. The 3B41RT data for 1 hourly rainfall estimates were used to make Intensity-Duration plot. Critical rainfall was estimated for every incidence by analysing cumulative rainfall leading to a landslide for total of 19 incidences and an empirical rainfall intensity-duration threshold for triggering shallow debris slides was developed (Intensity = 5.9 Duration-0.479).

Sea–ice concentration at 1 km resolution in summer from merged visible and microwave radiometer observations

2021 ◽  
Author(s):  
Valentin Ludwig ◽  
Gunnar Spreen
Keyword(s):  
Spatial Resolution ◽  
Microwave Radiometer ◽  
Thermal Infrared ◽  
Passive Microwave ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Statistical Consistency ◽  
Microwave Data ◽  
Infrared Data

<p>Sea–ice concentration, the surface fraction of ice in a given area, is a key component of the Arctic climate system, governing for example the ocean–atmosphere heat exchange. Satellite–based remote sensing offers the possibility for large–scale monitoring of the sea–ice concentration. Using passive microwave measurements, it is possible to observe the sea–ice concentration all year long, almost independently of cloud coverage. The spatial resolution of these measurements is limited to 5 km and coarser. Data from the visible and thermal infrared spectrum offer finer resolutions of 250 m–1 km, but need clear–sky scenes and, in case of visible data, sunlight. In previous work, we developed and analysed a merged dataset of passive microwave and thermal infrared data, combining AMSR2 and MODIS satellite data at 1 km spatial resolution. It has benefits over passive microwave data in terms of the finer spatial resolution and an enhanced potential for lead detection. At the same time, it outperforms thermal infrared data due to its spatially continuous coverage and the statistical consistency with the extensively evaluated passive microwave data. Due to higher surface temperatures in summer, the thermal–infrared based retrieval is limited to winter and spring months. In this contribution, we present first results of extending the existing dataset to summer by using visible data instead of thermal infrared data. The reflectance contrast between ice and water is used for the sea–ice concentration retrieval and results of merging visible and microwave data at 1 km spatial resolution are presented. Difficulties for both, the microwave and visual, data are surface melt processes during summer, which make sea–ice concentration retrieval more challenging. The merged microwave, infrared and visual dataset opens the possibility for a year–long, spatially continuous sea ice concentration dataset at a spatial resolution of 1 km.</p>

scholarly journals Nine-Year Systematic Evaluation of the GPM and TRMM Precipitation Products in the Shuaishui River Basin in East-Central China

2020 ◽  
Vol 12 (6) ◽  
pp. 1042 ◽  
Author(s):  
Xiaoying Yang ◽  
Yang Lu ◽  
Mou Leong Tan ◽  
Xiaogang Li ◽  
Guoqing Wang ◽  
...  
Keyword(s):  
River Basin ◽  
Time Scales ◽  
Daily Rainfall ◽  
Central China ◽  
Spatiotemporal Resolution ◽  
Study Region ◽  
East Central ◽  
Hourly Rainfall ◽  
Rainfall Estimates ◽  
Different Time Scales

Owing to their advantages of wide coverage and high spatiotemporal resolution, satellite precipitation products (SPPs) have been increasingly used as surrogates for traditional ground observations. In this study, we have evaluated the accuracy of the latest five GPM IMERG V6 and TRMM 3B42 V7 precipitation products across the monthly, daily, and hourly scale in the hilly Shuaishui River Basin in East-Central China. For evaluation, a total of four continuous and three categorical metrics have been calculated based on SPP estimates and historical rainfall records at 13 stations over a period of 9 years from 2009 to 2017. One-way analysis of variance (ANOVA) and multiple posterior comparison tests are used to assess the significance of the difference in SPP rainfall estimates. Our evaluation results have revealed a wide-ranging performance among the SPPs in estimating rainfall at different time scales. Firstly, two post-time SPPs (IMERG_F and 3B42) perform considerably better in estimating monthly rainfall. Secondly, with IMERG_F performing the best, the GPM products generally produce better daily rainfall estimates than the TRMM products. Thirdly, with their correlation coefficients all falling below 0.6, neither GPM nor TRMM products could estimate hourly rainfall satisfactorily. In addition, topography tends to impose similar impact on the performance of SPPs across different time scales, with more estimation deviations at high altitude. In general, the post-time IMERG_F product may be considered as a reliable data source of monthly or daily rainfall in the study region. Effective bias-correction algorithms incorporating ground rainfall observations, however, are needed to further improve the hourly rainfall estimates of the SPPs to ensure the validity of their usage in real-world applications.

The Tropical Rainfall Potential (TRaP) Technique. Part II: Validation

2005 ◽  
Vol 20 (4) ◽  
pp. 465-475 ◽  
Author(s):  
Ralph Ferraro ◽  
Paul Pellegrino ◽  
Michael Turk ◽  
Wanchun Chen ◽  
Shuang Qiu ◽  
...  
Keyword(s):  
Information Service ◽  
Storm Track ◽  
Stage Iv ◽  
Retrieval Algorithm ◽  
Maximum Rainfall ◽  
Tropical Rainfall ◽  
Hourly Rainfall ◽  
Potential Trap ◽  
Eta Model ◽  
Rainfall Estimates

Abstract Satellite analysts at the Satellite Services Division (SSD) of the National Environmental, Satellite, Data, and Information Service (NESDIS) routinely generate 24-h rainfall potential for all tropical systems that are expected to make landfall within 24 to at most 36 h and are of tropical storm or greater strength (>65 km h−1). These estimates, known as the tropical rainfall potential (TRaP), are generated in an objective manner by taking instantaneous rainfall estimates from passive microwave sensors, advecting this rainfall pattern along the predicted storm track, and accumulating rainfall over the next 24 h. In this study, the TRaPs generated by SSD during the 2002 Atlantic hurricane season have been validated using National Centers for Environmental Prediction (NCEP) stage IV hourly rainfall estimates. An objective validation package was used to generate common statistics such as correlation, bias, root-mean-square error, etc. It was found that by changing the minimum rain-rate threshold, the results could be drastically different. It was determined that a minimum threshold of 25.4 mm day−1 was appropriate for use with TRaP. By stratifying the data by different criteria, it was discovered that the TRaPs generated using Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain rates, with its optimal set of measurement frequencies, improved spatial resolution, and advanced retrieval algorithm, produced the best results. In addition, the best results were found for TRaPs generated for storms that were between 12 and 18 h from landfall. Since the TRaP is highly dependent on the forecast track of the storm, selected TRaPs were rerun using the observed track contained in the NOAA/Tropical Prediction Center (TPC) “best track.” Although some TRaPs were not significantly improved by using this best track, significant improvements were realized in some instances. Finally, as a benchmark for the usefulness of TRaP, comparisons were made to Eta Model 24-h precipitation forecasts as well as three climatological maximum rainfall methods. It was apparent that the satellite-based TRaP outperforms the Eta Model in virtually every statistical category, while the climatological methods produced maximum rainfall totals closer to the stage IV maximum amounts when compared with TRaP, although these methods are for storm totals while TRaP is for a 24-h period.

A new methodology for rain identification from passive microwave data in the Tropics using neural networks

2013 ◽  
Vol 139 (673) ◽  
pp. 912-922 ◽  
Author(s):  
Sahra Kacimi ◽  
Nicolas Viltard ◽  
Pierre-Emmanuel Kirstetter
Keyword(s):  
Neural Networks ◽  
Passive Microwave ◽  
The Tropics ◽  
Microwave Data

scholarly journals Evaluation of Coincident Passive Microwave Rainfall Estimates Using TRMM PR and Ground Measurements as References

2008 ◽  
Vol 47 (12) ◽  
pp. 3170-3187 ◽  
Author(s):  
Xin Lin ◽  
Arthur Y. Hou
Keyword(s):  
United States ◽  
Tropical Rainfall Measuring Mission ◽  
Passive Microwave ◽  
Earth Observing System ◽  
Temporal Sampling ◽  
Trmm Pr ◽  
Microwave Imager ◽  
Cross Track ◽  
Microwave Sensors ◽  
Rainfall Estimates

Abstract This study compares instantaneous rainfall estimates provided by the current generation of retrieval algorithms for passive microwave sensors using retrievals from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and merged surface radar and gauge measurements over the continental United States as references. The goal is to quantitatively assess surface rain retrievals from cross-track scanning microwave humidity sounders relative to those from conically scanning microwave imagers. The passive microwave sensors included in the study are three operational sounders—the Advanced Microwave Sounding Unit-B (AMSU-B) instruments on the NOAA-15, -16, and -17 satellites—and five imagers: the TRMM Microwave Imager (TMI), the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) instrument on the Aqua satellite, and the Special Sensor Microwave Imager (SSM/I) instruments on the Defense Meteorological Satellite Program (DMSP) F-13, -14, and -15 satellites. The comparisons with PR data are based on “coincident” observations, defined as instantaneous retrievals (spatially averaged to 0.25° latitude and 0.25° longitude) within a 10-min interval collected over a 20-month period from January 2005 to August 2006. Statistics of departures of these coincident retrievals from reference measurements as given by the TRMM PR or ground radar and gauges are computed as a function of rain intensity over land and oceans. Results show that over land AMSU-B sounder rain retrievals are comparable in quality to those from conically scanning radiometers for instantaneous rain rates between 1.0 and 10.0 mm h−1. This result holds true for comparisons using either TRMM PR estimates over tropical land areas or merged ground radar/gauge measurements over the continental United States as the reference. Over tropical oceans, the standard deviation errors are comparable between imager and sounder retrievals for rain intensities above 5 mm h−1, below which the imagers are noticeably better than the sounders; systematic biases are small for both imagers and sounders. The results of this study suggest that in planning future satellite missions for global precipitation measurement, cross-track scanning microwave humidity sounders on operational satellites may be used to augment conically scanning microwave radiometers to provide improved temporal sampling over land without degradation in the quality of precipitation estimates.

Sign in / Sign up

Export Citation Format

Share Document