A Next-generation Microwave Rainfall Retrieval Algorithm for use by TRMM and GPM

2007 ◽  
pp. 235-252 ◽  
Author(s):  
Christian Kummerow ◽  
Hirohiko Masunaga ◽  
Peter Bauer
Keyword(s):  
Retrieval Algorithm ◽  
Next Generation ◽  
Rainfall Retrieval

A scattering-based over-land rainfall retrieval algorithm for South Korea using GCOM-W1/AMSR-2 data

2017 ◽  
Vol 53 (3) ◽  
pp. 385-392 ◽  
Author(s):  
Young-Joo Kwon ◽  
Hayan Shin ◽  
Hyunju Ban ◽  
Yang-Won Lee ◽  
Kyung-Ae Park ◽  
...  
Keyword(s):  
South Korea ◽  
Retrieval Algorithm ◽  
Rainfall Retrieval

A Citizen Observatory at schools to train a rainfall retrieval algorithm based on Earth Observation

2020 ◽  
Author(s):  
Sandra de Vries ◽  
Monica Estebanez Camarena
Keyword(s):  
Climate Change ◽  
Data Collection ◽  
West Africa ◽  
Rain Gauge ◽  
Earth Observation ◽  
Rainfall Data ◽  
Northern Ghana ◽  
Retrieval Algorithm ◽  
Educational Module ◽  
Rainfall Retrieval

<p>West Africa’s economy is mainly sustained on agriculture and over 70% of crops are rain-fed. Economic growth and food security in this region is therefore highly dependent on the knowledge of rainfall patterns. According to the IPCC, the Global South will seriously suffer from climate change. As traditional rainfall patterns shift, accurate rainfall information becomes crucial for farmers to optimize food production.</p><p>The scarce rain gauge distribution and data transmission challenges make rainfall analysis difficult in these regions. Satellites could offer a solution to this problem, but present satellite products do not account for local characteristics and perform poorly in West Africa.</p><p>A rainfall retrieval algorithm, developed within the Schools and Satellites (SaS) project, could overcome the lack of ground data and good rainfall satellite products through earth observation and advanced machine learning. However, to validate such an algorithm requires a high amount of rainfall data from ground stations. Since rain gauges are scarce in West Africa, a (temporary) high density observation network is necessary to strengthen the training and validation dataset provided by TAHMO and GMet ground measurements. SaS therefore engages with schools in Northern Ghana to build a Citizen Observatory. </p><p>SaS is being funded by the European Space Agency as one of the pilot projects of CSEOL (Citizen Science and Earth Observation Lab). It is being developed in a cooperation between TU Delft, PULSAQUA, TAHMO Ghana, Smartphones4Water (S4W) and GMet. The Proof-of-Concept Algorithm will be fed with data collected in the Citizen Observatory during the rainy season of 2020.</p><p>This Citizen Observatory will be built around the already existing infrastructure of a classroom where Climate Change is amongst the topics in the Ghanaian teaching curriculum. We aim to provide a Climate Change educational module that can be used directly by the teachers. The educational module incorporates the building of their own low-cost rain gauge to be used for manual rainfall data collection. This rainfall collection method has already been highly tested by S4W in Nepal. Students will design their own research around the daily rainfall measurements, which they will submit via a web application called Open Data Kit (ODK). The data is being validated by including a picture of the rainfall measurement that is checked with the number passed on by the citizen scientist.</p><p>The Citizen Observatory will be placed under the existing TAHMO and S4W infrastructures to respectively continue the interaction with schools and to continue data collection, -validation and -visualization. If the algorithm proofs to indeed perform better than current satellite products for the pilot area in Northern Ghana, the Citizen Observatory could in the future help to validate and improve the product for the whole of West-Africa.</p><p>To enable the use of this Citizen Observatory for management of water resources and in this case more and better rainfall data, much effort is needed. We will demonstrate which measures we have taken to ensure that the Citizen Observatory performs with enough quality, and how (if done well) it has the potential to increase the impact of this study.</p>

scholarly journals A measurement campaign to assess sources of error in microwave link rainfall estimation

2018 ◽  
Vol 11 (8) ◽  
pp. 4645-4669 ◽  
Author(s):  
Thomas C. van Leth ◽  
Aart Overeem ◽  
Hidde Leijnse ◽  
Remko Uijlenhoet
Keyword(s):  
Communication Networks ◽  
Global Analysis ◽  
Time Lapse ◽  
Rain Gauge ◽  
Operating Conditions ◽  
Retrieval Algorithm ◽  
Measurement Campaign ◽  
Temporal Sampling ◽  
Microwave Link ◽  
Rainfall Retrieval

Abstract. We present a measurement campaign to address several error sources associated with rainfall estimates from microwave links in cellular communication networks. The core of the experiment is provided by three co-located microwave links installed between two major buildings on opposite sides of the small town of Wageningen, approximately 2 km apart: a 38 GHz formerly commercial microwave link, as well as 26 and 38 GHz (dual-polarization) research microwave links. Transmitting and receiving antennas have been attached to masts installed on the roofs of the two buildings, about 30 m above the ground. This setup was complemented with an infrared large-aperture scintillometer, installed over the same path, as well as five laser disdrometers positioned at several locations along the path and an automated rain gauge. Temporal sampling of the received signals was performed at a rate of 20 Hz. The setup was monitored by time-lapse cameras to assess the state of the antennas as well as the atmosphere. The experiment was active between August 2014 and December 2015. Data from an existing automated weather station situated just outside Wageningen was further used to compare and to interpret the findings. In addition to presenting the experiment, we also conduct a preliminary global analysis and show several cases highlighting the different phenomena affecting received signal levels: rainfall, solid precipitation, temperature, fog, antenna wetting due to rain or dew, and clutter. We also briefly explore cases where several phenomena play a role. A rainfall intensity (R) – specific attenuation (k) relationship was derived from the disdrometer data. We find that a basic rainfall retrieval algorithm without corrections already provides a reasonable correlation to rainfall as measured by the disdrometers. However, there are strong systematic overestimations (factors of 1.2–2.1) which cannot be attributed to the R–k relationship. We observe attenuations in the order of 3 dB due to antenna wetting under fog or dew conditions. We also observe fluctuations of a similar magnitude related to changes in temperature. The response of different makes of microwave antennas to many of these phenomena is significantly different even under the exact same operating conditions and configurations.

scholarly journals The Passive Microwave Neural Network Precipitation Retrieval (PNPR) Algorithm for the CONICAL Scanning Global Microwave Imager (GMI) Radiometer

2018 ◽  
Vol 10 (7) ◽  
pp. 1122 ◽  
Author(s):  
Paolo Sanò ◽  
Giulia Panegrossi ◽  
Daniele Casella ◽  
Anna Marra ◽  
Leo D’Adderio ◽  
...  
Keyword(s):  
Neural Network ◽  
Selection Procedure ◽  
Rainfall Rate ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Input Selection ◽  
Channel Response ◽  
Observational Database ◽  
Microwave Imager ◽  
Rainfall Retrieval

This paper describes a new rainfall rate retrieval algorithm, developed within the EUMETSAT H SAF program, based on the Passive microwave Neural network Precipitation Retrieval approach (PNPR v3), designed to work with the conically scanning Global Precipitation Measurement (GPM) Microwave Imager (GMI). A new rain/no-rain classification scheme, also based on the NN approach, which provides different rainfall masks for different minimum thresholds and degree of reliability, is also described. The algorithm is trained on an extremely large observational database, built from GPM global observations between 2014 and 2016, where the NASA 2B-CMB (V04) rainfall rate product is used as reference. In order to assess the performance of PNPR v3 over the globe, an independent part of the observational database is used in a verification study. The good results found over all surface types (CC > 0.90, ME < −0.22 mm h−1, RMSE < 2.75 mm h−1 and FSE% < 100% for rainfall rates lower than 1 mm h−1 and around 30–50% for moderate to high rainfall rates), demonstrate the good outcome of the input selection procedure, as well as of the training and design phase of the neural network. For further verification, two case studies over Italy are also analysed and a good consistency of PNPR v3 retrievals with simultaneous ground radar observations and with the GMI GPROF V05 estimates is found. PNPR v3 is a global rainfall retrieval algorithm, able to optimally exploit the GMI multi-channel response to different surface types and precipitation structures, that provide global rainfall retrieval in a computationally very efficient way, making the product suitable for near-real time operational applications.

scholarly journals A New Data Fusion Neural Network Scheme for Rainfall Retrieval Using Passive Microwave and Visible/Infrared Satellite Data

2021 ◽  
Vol 11 (10) ◽  
pp. 4686
Author(s):  
Massimiliano Sist ◽  
Giovanni Schiavon ◽  
Fabio Del Frate
Keyword(s):  
Data Fusion ◽  
Low Earth Orbit ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Earth Orbit ◽  
Test Cases ◽  
Orbit Satellite ◽  
Infrared Imager ◽  
Rainfall Retrieval ◽  
Infrared Data

A new data fusion technique based on Artificial Neural Networks (ANN) for the design of a rainfall retrieval algorithm is presented. The use of both VIS/IR (VISible and InfraRed) data from GEO (Geostationary Earth Orbit) satellite and of passive microwave data from LEO (Low Earth Orbit) satellite can take advantage of both types of sensors reducing their limitations. The technique can reconstruct the surface rain field with the MSG-SEVIRI (Meteosat Second Generation–Spinning Enhanced Visible Infrared Imager) spatial and temporal resolution, which means 3 km at the sub satellite point and 5 km at mid-latitudes, every 15 min, respectively. Rainfall estimations are also compared with H-SAF (Hydrology Satellite Application Facility) PR-OBS3A operational product showing better performance both on the identification of rainy areas and on the retrieval of the amount of precipitation. In particular, in the considered test cases, results report an improvement in average of 83% in terms of probability of rainy areas detection, of 45% in terms of false alarm rate, and of 47% in terms of root mean square error in the retrieval of the amount of precipitation.

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