scholarly journals A daily 25 km short-latency rainfall product for data-scarce regions based on the integration of the Global Precipitation Measurement mission rainfall and multiple-satellite soil moisture products

2020 ◽  
Vol 24 (5) ◽  
pp. 2687-2710 ◽  
Author(s):  
Christian Massari ◽  
Luca Brocca ◽  
Thierry Pellarin ◽  
Gab Abramowitz ◽  
Paolo Filippucci ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Resource Management ◽  
Water Resource ◽  
Water Resource Management ◽  
Short Latency ◽  
Precipitation Measurement ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Rainfall Estimates

Abstract. Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries. For instance, in some regions in Africa the gauge density is often less than one station per 10 000 km2. The availability of rainfall data provided by gauges is also not always guaranteed in near real time or with a timeliness suited for agricultural and water resource management applications, as gauges are also subject to malfunctions and regulations imposed by national authorities. A potential alternative is satellite-based rainfall estimates, yet comparisons with in situ data suggest they are often not optimal. In this study, we developed a short-latency (i.e. 2–3 d) rainfall product derived from the combination of the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement) Early Run (IMERG-ER) with multiple-satellite soil-moisture-based rainfall products derived from ASCAT (Advanced Scatterometer), SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active and Passive) L3 (Level 3) satellite soil moisture (SM) retrievals. We tested the performance of this product over four regions characterized by high-quality ground-based rainfall datasets (India, the conterminous United States, Australia and Europe) and over data-scarce regions in Africa and South America by using triple-collocation (TC) analysis. We found that the integration of satellite SM observations with in situ rainfall observations is very beneficial with improvements of IMERG-ER up to 20 % and 40 % in terms of correlation and error, respectively, and a generalized enhancement in terms of categorical scores with the integrated product often outperforming reanalysis and ground-based long-latency datasets. We also found a relevant overestimation of the rainfall variability of GPM-based products (up to twice the reference value), which was significantly reduced after the integration with satellite soil-moisture-based rainfall estimates. Given the importance of a reliable and readily available rainfall product for water resource management and agricultural applications over data-scarce regions, the developed product can provide a valuable and unique source of rainfall information for these regions.

scholarly journals A daily/25 km short-latency rainfall product for data scarce regions based on the integration of the GPM IMERG Early Run with multiple satellite soil moisture products

2019 ◽  
Author(s):  
Christian Massari ◽  
Luca Brocca ◽  
Thierry Pellarin ◽  
Gab Abramowitz ◽  
Paolo Filippucci ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Resource Management ◽  
Water Resource ◽  
Water Resource Management ◽  
Short Latency ◽  
In Situ Data ◽  
Long Latency ◽  
Agricultural Applications ◽  
Unique Source

Abstract. Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries. For instance, in some regions in Africa the gauge density is often less than one station per 10 000 km2. The availability of rainfall data provided by gauges is also not always guaranteed in near real time or with a timeliness suited for agricultural and water resource management applications as gauges are also subject to malfunctions and regulations imposed by national authorities. A potential alternative are satellite-based rainfall estimates, yet comparisons with in-situ data suggest they're often not optimal. In this study, we developed a short-latency (i.e., 2–3 days) rainfall product derived from the combination of the Integrated Multi-Satellite Retrievals for GPM early run (IMERG-ER) with multiple satellite soil moisture-based rainfall products derived from ASCAT, SMOS and SMAP L3 satellite soil moisture (SM) retrievals. We tested the performance of this product over four regions characterized by high quality ground-based rainfall datasets (India, Conterminous United States, Australia and Europe) and over data scarce regions in Africa and South America by using Triple Collocation analysis (TC). We found the integration of satellite SM observations with in-situ rainfall observations is very beneficial with improvements of IMERG-ER up to 20 % and 40 % in terms of correlation and error, respectively, and a generalized enhancement in terms of categorical scores with the integrated product often outperforming reanalysis and ground-based long latency datasets. Given the importance of a reliable and readily available rainfall product for water resource management and agricultural applications over data scarce regions, the developed product can provide a valuable and unique source of rainfall information for these regions.

Microphysical features indicated by GPM DPR product “flagHeavyIcePrecip” – Case studies on lightning activity

2020 ◽  
Author(s):  
Kenji Suzuki ◽  
Rimpei Kamamoto ◽  
Tetsuya Kawano ◽  
Katsuhiro Nakagawa ◽  
Yuki Kaneko
Keyword(s):  
Heavy Rain ◽  
Lightning Activity ◽  
Dual Frequency ◽  
Convective Clouds ◽  
Precipitation Measurement ◽  
X Band ◽  
Precipitation Type ◽  
Global Precipitation Measurement ◽  
Global Precipitation

<p>Two products from the Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) algorithms, a flag of intense solid precipitation above the –10°C height (“flagHeavyIcePrecip”), and a classification of precipitation type (“typePrecip”) were validated quantitatively from the viewpoint of microphysics using ground-based in-situ hydrometeor measurements and X-band multi-parameter (X-MP) radar observations of snow clouds that occurred on 4 February 2018. The distribution of the “flagHeavyIcePrecip” footprints was in good agreement with that of the graupel-dominant pixels classified by the X-MP radar hydrometeor classification. In addition, the vertical profiles of X-MP radar reflectivity exhibited significant differences between footprints flagged and unflagged by “flagHeavyPrecip”. We confirmed the effectiveness of “flagHeavyIcePrecip”, which is built into “typePrecip” algorithm, for detecting intense ice precipitation and concluded that "flagHeavyIcePrecip" is appropriate to useful for determining convective clouds.</p><p>It is well known that the lightning activity is closely related to the convection. We examined the lightning activity using GPM DPR product flagHeavyIcePrecip that indicates the existence of graupel in the upper cloud. On 20 June 2016, we experienced heavy rain with active lightning during Baiu monsoon rainy season, while the GPM DPR passed over Kyushu region in Japan. The distribution of “flagHeavyIcePrecip” obtained from the GPM DPR well corresponded to the CG/IC lightning concentration. On 4 September 2019, isolated thunder clouds observed by the GPM DPR was also similar to the “flagHeavyIcePrecip” distribution. However, partially there was IC lightning without “flagHeavyIcePrecip”, which was positive lightning. It was suggested to have been produced in the upper clouds in which positive ice crystals were dominant.</p>

Deepening our understanding of shallow precipitation measurements from space

2020 ◽  
Author(s):  
Linda Bogerd ◽  
Hidde Leijnse ◽  
Aart Overeem ◽  
Remko Uijlenhoet
Keyword(s):  
Research Area ◽  
Level Ii ◽  
Precipitation Measurement ◽  
Ground Validation ◽  
Precipitation Type ◽  
Project Structure ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
The Impact

<p><span><span>Satellite-based remote sensing provides a unique opportunity for the estimation of global precipitation patterns. </span><span>In order to use this approach, it is crucial that the uncertainty in the satellite estimations is precisely understood. T</span><span>he retrieval</span><span> of high-latitude precipitation </span><span>(especially shallow precipitation) </span><span>remains challenging for satellite precipitation monitoring. </span><span>This </span><span>project</span><span> will quantify the quality of the precipitation estimations obtained from</span> <span>the Global Precipitation Measurement (GPM) mission, where the focus will be on the level II and III products.</span> <span>Initially, t</span><span>h</span><span>e </span><span>Netherlands </span><span>is chosen as research area, since it has an excellent infrastructure with both in-situ and remotely sensed ground-based precipitation measurements, </span><span>its flat topography,</span> <span>and the </span><span>frequent</span> <span>occurrence</span><span> of shallow precipitation events. The project will study the influence of precipitation type and the impact of the seasons on the accuracy </span><span>of the GPM products. </span><span>Hereafter, the project will focus on the physical causes behind the discrepancies between the GPM products and the ground validation</span><span>, </span><span>w</span><span>hich can be used to improve the </span><span>retrieval</span><span> algorithms. The </span><span>presentation</span><span> will outline the project structure and will demonstrate </span><span>the</span> <span>initial</span><span> results. </span></span></p>

Development of a consistent soil moisture decadal data record from multiple satellites

2020 ◽  
Author(s):  
Steven Chan
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Microwave Radiometer ◽  
Microwave Remote Sensing ◽  
Precipitation Measurement ◽  
X Band ◽  
Data Record ◽  
System Data ◽  
Global Precipitation Measurement ◽  
Global Precipitation

<p>In recent decades, passive microwave remote sensing at lower frequencies (1-10 GHz) has become a primary means to routinely monitor soil moisture on a global scale. Despite the success of a number of L- and C/X-band radiometers independently developed and launched by various government agencies over the last two decades, there has not been a concerted effort to leverage the combined brightness temperature (T<sub>B</sub>) observations from these instruments to derive an integrated soil moisture data record within a consistent geophysical inversion framework. The availability of such a consistent data record would provide critical insights into the dynamics of surface hydrological processes, including anomaly detection, interannual variability, and monitoring of the onset and evolution of long-term spatial and temporal variability due to natural or anthropogenic changes in land surface conditions.</p><p>Recent advances in T<sub>B</sub> intercalibration on current and historical satellites have resulted in the availability of consistent T<sub>B</sub> observations that extend from years to decades. For passive microwave remote sensing of soil moisture, satellite intercalibration undertaken by the Global Precipitation Measurement (GPM) mission [1-2] has resulted in a decadal repository of intercalibrated T<sub>B</sub> observations at X-band (10.7 GHz) frequencies from GMI (2014-present), AMSR2 (2012-present), WindSat (2003-present), TMI (1997-2015) and AMSR-E (2002-2011). Likewise, recent studies on relative calibration by SMOS (2009-present) and SMAP (2015-present) teams have also enabled the production of a similar repository of intercalibrated T<sub>B</sub> observations for soil moisture estimation at L-band (1.41 GHz) frequencies [3]. When used as inputs to a common geophysical inversion model, these T<sub>B</sub> observations can be used for soil moisture estimation. Because consistency has been reinforced at the level of T<sub>B</sub> observations among satellites, the resulting record of soil moisture retrieval is expected to exhibit the same internal consistency. Together, therefore, these T<sub>B</sub> repositories provide the foundation for the development of current and historical consistent soil moisture data records with more frequent and wider coverage than any single satellite can achieve alone.</p><p>In this presentation, we will describe a NASA-funded initiative [4] (MEaSUREs: Making Earth System Data Records for use in Research Environments) to create a consistent soil moisture decadal data record from multiple satellites for terrestrial hydrological applications. Preliminary results, ancillary data preparation, product delivery schedule, and deliverables of this initiative will be discussed in this presentation.</p><p>References:</p><ol><li>Berg, W., S. Bilanow, R. Chen, S. Datta, D. Draper, H. Ebrahimi, S. Farrar, W. Jones, R. Kroodsma, D. McKague, V. Payne, J. Wang, T. Wilheit, and J. Yang. 2016. “Intercalibration of the GPM Microwave Radiometer Constellation,” J. Atmos. Oceanic Technol., 33, pp. 2639–2654, doi: 10.1175/JTECH-D-16-0100.1.</li> <li>Biswas, S. K., S. Farrar, K. Gopalan, A. Santos-Garcia, W. L. Jones and S. Bilanow. 2013. “Intercalibration of Microwave Radiometer Brightness Temperatures for the Global Precipitation Measurement Mission,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 3, pp. 1465–1477. doi: 10.1109/TGRS.2012.2217148.</li> <li>Bindlish, R., S. Chan, T. Jackson, A. Colliander, and Y. Kerr. 2018. “Integration of SMAP and SMOS Observations,” 2018 IEEE IGARSS, Valencia, Spain.</li> <li>"MEaSUREs: Making Earth System Data Records for Use in Research Environments," Accessed Nov 8, 2018. [Online]. Available: https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects</li> </ol>

A review of the methods available for estimating soil moisture and its implications for water resource management

2012 ◽  
Vol 458-459 ◽  
pp. 110-117 ◽  
Author(s):  
Pariva Dobriyal ◽  
Ashi Qureshi ◽  
Ruchi Badola ◽  
Syed Ainul Hussain
Keyword(s):  
Soil Moisture ◽  
Resource Management ◽  
Water Resource ◽  
Water Resource Management

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.

Water Resource Management

2022 ◽  
pp. 197-218
Author(s):  
Satya Prakash ◽  
Pinakana Sai Deepak
Keyword(s):  
Big Data ◽  
Resource Management ◽  
Water Resource ◽  
Water Resource Management ◽  
Satellite Images ◽  
Measurement Techniques ◽  
Satellite Image ◽  
Quality Parameters ◽  
In Situ Measurement

Water is an essential component for the survival of mankind and for balancing the ecosystem and livelihood. The world is experiencing a scarcity of water, both in terms of quality and quantity. Although there are several in-situ measurement techniques, they seem insufficient for large areas involving several parameters. Analysis of satellite images for estimating the quality and quantity of natural water has become an accepted tool for better spatial planning. With the increase in variety, volume, and velocity of satellite image, a tool for faster and accurate processing of the data is needed. Google Earth Engine (GEE) is one such cloud-based geo-big data platform. This chapter reviews the work of several researchers worldwide who have used and demonstrated the capability of satellite images with other geo-big data such as elevation, landcover, etc. for water resource management on the GEE platform. It can be concluded from the review work that GEE can help in estimating the water quality parameters with reasonable accuracy, comparable to the in-situ measurement, albeit quickly.

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