PosterPDF Available

GNSS and SAR water vapor products for the enhancement of heavy rainfall prediction in Africa: planned activities and preliminary results within the H2020 TWIGA project

Poster

GNSS and SAR water vapor products for the enhancement of heavy rainfall prediction in Africa: planned activities and preliminary results within the H2020 TWIGA project

Abstract

The activities carried out within the WP2 of the TWIGA-H2020 project are described. In particular, the following topics are covered: the definition of a new low-cost GNSS receiver network to be soon installed in Uganda, a new algorithm for the retrieval of water vapor maps from Sentinel-1 data and the ongoing numerical weather prediction experiments.
Sentinel SAR APSs
SAR and GNSS signals are affected by the same water vapor induced delay along their path
through the troposphere. A novel approach to the estimation of this effect in the
interferometric SAR images will be applied to the Sentinel Images of the TWIGA sub
Saharan regions. This approach is currently undergoing a validation procedure on Italian
experimental test sites. The approach produces a sequence of differential water vapor
delay maps calibrated for residual orbital errors thanks to a synergic use of SAR and GNSS.
The differential maps are referred to a common ‘MASTER’ unknown map of water vapor
delay. This MASTER has to be determined, by using external sources, and added back to
the differential maps to get a sequence of absolute ZTD maps. Different approaches are
under analysis, exploiting ZTD maps from GACOS (Yu et al, 2018)or derived from the
assimilation of GNSS ZTD in WRF (see on the right) by the 3dVAR tecnique (Barker et al,
2012), according to Pichelli et al.2017.
GNSS and SAR water vapor products for the enhancement of heavy rainfall prediction in Africa:
planned activities and preliminary results within the H2020 TWIGA project
G. Venuti(1), E. Realini(2), G. Tagliaferro(2), A. Gatti(2), A. N. Meroni(1,3), S. Barindelli(1), A. Monti-Guarnieri(1), M. Manzoni(1), L. Pertusini(2)
(1) Politecnico di Milano, Geomatics and Earth Observation Laboratory (GEOlab), Piazza Leonardo da Vinci 32, 20133 Milano, Italy ; (2) Geomatics Research & Development srl (GReD), Italy, (3)CIMA Research Foundation, Italy
giovanna.venuti@polimi.it, eugenio.realini@g-red.eu,giulio.tagliaferro@g-red.eu, andrea.gatti@g-red.eu, agostino.meroni@cimafoundation.com, stefano.barindelli@polimi.it, andrea.montiguarnieri@polimi.it, marco.manzoni@polimi.it, lisa.pertusini@g-red.eu
Atmospheric water vapor monitoring within TWIGA-H2020 project
The prediction of heavy rainfall is a critical issue in several countries. In Africa, the scarcity of data to support such predictions makes it fundamental to improve the monitoring of atmospheric parameters. This is one of the objectives of the H2020 project TWIGA - Transforming Weather Water data into value-added Information services for
sustainable Growth in Africa. Among other objectives, the project will allow the development of a service for delivering atmospheric water vapor observations to meteorological agencies for the assimilation into Numerical Weather Models, NWPs. More precisely, satellite data, coming from Sentinel 1 SAR images, will be used to derive
Atmospheric Phase Screens, APSs; Zenith Total Delays, ZTDs will be obtained from GNSS observations collected by ad hoc networks of low-cost stations. After proper calibration and validation procedures the delay maps from SAR and the delay time series from GNSS will be assimilated into NWMs to improve the prediction of heavy rainfall. An
overview of activities carried out during the first year of the project are here reported.
References:
Barindelli S., Realini E., Venuti G., Fermi A., Gatti A. (2018). Detection of water vapor time variations associated with heavy rain in northern Italy by geodetic and low-cost GNSS receivers. Earth, Planets and Space, 70(1), 28.
Deng Z., Bender M., Dick G., Ge M., Wickert J., Ramatschi M., Zou X. (2009). Retrieving tropospheric delays from GPS networks densified with single frequency receivers. Geophys Res Lett 36(19): L19802.
Herrera A.M., Suhandri H.F., Realini E., Reguzzoni M., de Lacy M.C. (2015). goGPS: open-source MATLAB software, GPS Solutions, Available online 19 June 2015 (DOI 10.1007/s10291-015-0469-x; ISSN 1080-5370).
Pichelli E., Ferretti R., Cimini D. , Panegrossi G., Perissin D., Pierdicca N., Rocca F., and Rommen B. (2015). InSAR Water Vapor Data Assimilation into Mesoscale Model MM5: Technique and Pilot Study, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8 (8), pp. 3859-3875,
doi:10.1109/JSTARS.2014.2357685,.
Realini E., Sato K., Tsuda T., Susilo, Manik T. (2014). An observation campaign of precipitable water vapor with multiple GPS receivers in western Java, Indonesia, Progress in Earth and Planetary Science, 1 (17), (DOI 10.1186/2197-4284-1-17).
Sato K., Realini E., Tsuda T., Oigawa M., Iwaki Y., Shoji Y., Seko H. (2013). A High-Resolution Precipitable Water Vapor Monitoring System Using a Dense Network of GNSS Receivers. Journal of Disaster Research 8 (1), 37-47, (ISSN 1881-2473).
Skamarock W. C., Klemp J. B., Dudhia J., Gill D. O., Barker D. M., Duda M., et al. (2008). A description of the advanced research WRF version 3 (NCAR Technical Note NCAR/TN-475+STR). p. 113.https://doi.org/10.5065/D68S4MVH
Yu C., Li Z., Penna N. T., Crippa, P. (2018). Generic atmospheric correction model for Interferometric Synthetic Aperture Radar observations. Journal of Geophysical Research: Solid Earth, 123.https://doi.org/10.1029/2017JB015305
Barker D., Huang X., Liu Z., Auligné T., Zhang X., Rugg S., Ajjaji R., Bourgeois A., Bray J., Chen Y., Demirtas M., Guo Y., Henderson T., Huang W., Lin H., Michalakes J., Rizvi S., Zhang X. (2012). The Weather Research and Forecasting Model's Community Variational/Ensemble Data Assimilation System: WRFDA. Bull. Amer. Meteor. Soc., 93,
831843.
EGU2019 - X3.80
Acknowledgments
TWIGA has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No.776691.
TAHMO: We thank the Trans-African Hydro-Meteorological Observatory (TAHMO) for the provision of meteorological data. Interested parties may
contact TAHMO.org for these data.
GACOS is supported by the UK NERC through the Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET, ref.:
come30001) and the LICS project (ref. NE/K010794/1), and the ESA-MOST DRAGON-4 project (ref. 32244). We acknowledge European Centre for
Medium-Range Weather Forecasts (ECMWF) for their operational high resolution tropospheric products. We also acknowledge Professor Geoffrey
Blewitt and his team at Nevada Geodetic Laboratory, University of Nevada, Reno for sharing their GPS tropospheric delay products.
General Assembly - Vienna | Austria | 712 April 2019
GNSS ZTDs from low-cost sensors
Two networks of low-cost GNSS monitoring units (produced by Softeco srl) will be
deployed over the TWIGA test sites of Uganda and Ghana. A first set of 6 receivers will be
delivered to Makerere University, Uganda within the end of April. The receivers will be co-
located with existent meteorological stations of the TAHMO network.
The data will be processed by using the new version of the open software goGPS (Herrera
et al.2014)by GReD, implementing a PPP positioning strategy and a batch least squares
solution. The ionospheric component of the atmospheric delay will be locally estimated by
exploiting the available dual frequency geodetic stations and the SEID Approach (Deng et
al.2009).
NWP model assimilation
The Weather and Research Forecasting (WRF) model (Skamarock et al, 2008) has been
selected to perform TWIGA assimilation experiments, in which GNSS and SAR ZTD
products will be assimilated through the 3dVAR technique (Barker et al, 2012). Up to now,
experiments to define the optimal assimilation procedure have been done in Italy.
Sensitivity tests on the setup of the model for the prediction of heavy rain events in
tropical regions are ongoing, to account for the specific dynamics of those areas.
With the aim of demonstrating that assimilating ZTD products can improve the forecast of
convective storms (both in terms of timing and position), we started by selecting two case
studies of heavy rains in South Africa (January 2017 and March 2018), where GNSS data
from geodetic permanent stations are freely available. The selection was initially done by
looking at the Floodlist archive (http://floodlist.com/). A validation against in situ
observations (both from TAHMO network and from the SAWS network) will follow.
Figure 1: Preliminary design of the Uganda low cost GNSS network. The selected sites are already equipped with the
TAHMO meteorological stations. Five geodetic permanent stations are already available in the area. A picture of a low
cost GNSS monitoring unit with the solar panel is shown on the right.
Figure 3: Copernicus EU Sentinel-1 A/B acquisitions extension
for TWIGA.
Figure 2: Original Copernicus EU Sentinel-1 A/B acquisitions
in Africa.
Figure 5: GNSS ZTD time series of some geodetic
permanent stations in the area of the heavy precipitation
event of January 2017. goGPS PPP processing.
Figure 4: WRF numerical domains (d01, d02, d03) at 13.5,
4.5 and 1.5 km horizontal resolutions and the location of all
the geodetic stations available. The color shading show the
model orography
http://twiga-h2020.eu
TWIGA-H2020 objective and new paradigm
Provide currently unavailable geo-information on weather, water and climate for sub-
Saharan Africa by enhancing satellite-based geo-data with innovative in situ sensors and
developing related information services that answer needs of African stakeholders and the
GEOSS community.
old and
new TWIGA paradigm
Zenith Troposheric Delays (ZTDs) from GNSS observations will be used to calibrate
Sentinel SAR Atmospheric Phase Screens (APSs).
Both ZTD time series and maps will be assimilated into Numerical Weather Prediction
Models (NWP) to enhance the predictability of convective storms.
SAR raw data processing water vapor maps/APS
GNSS raw data processing water vapor time series/GNSS-ZTD
APS + GNSS-ZTD calibration SAR-ZTD maps
SAR-ZTD &/or GNSS-ZTD NWP assimilation predictions products
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  • A M Herrera
  • H F Suhandri
  • E Realini
  • M Reguzzoni
  • M C De Lacy
Herrera A.M., Suhandri H.F., Realini E., Reguzzoni M., de Lacy M.C. (2015). goGPS: open-source MATLAB software, GPS Solutions, Available online 19 June 2015 (DOI 10.1007/s10291-015-0469-x; ISSN 1080-5370).
InSAR Water Vapor Data Assimilation into Mesoscale Model MM5: Technique and Pilot Study
  • E Pichelli
  • R Ferretti
  • D Cimini
  • G Panegrossi
  • D Perissin
  • N Pierdicca
  • F Rocca
Pichelli E., Ferretti R., Cimini D., Panegrossi G., Perissin D., Pierdicca N., Rocca F., and Rommen B. (2015). InSAR Water Vapor Data Assimilation into Mesoscale Model MM5: Technique and Pilot Study, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8 (8), pp. 3859-3875, doi:10.1109/JSTARS.2014.2357685,.
An observation campaign of precipitable water vapor with multiple GPS receivers in western Java
  • E Realini
  • K Sato
  • T Tsuda
  • Susilo
  • T Manik
Realini E., Sato K., Tsuda T., Susilo, Manik T. (2014). An observation campaign of precipitable water vapor with multiple GPS receivers in western Java, Indonesia, Progress in Earth and Planetary Science, 1 (17), (DOI 10.1186/2197-4284-1-17).
A description of the advanced research WRF version 3 (NCAR Technical Note NCAR/TN-475+STR)
  • W C Skamarock
  • J B Klemp
  • J Dudhia
  • D O Gill
  • D M Barker
  • M Duda
Skamarock W. C., Klemp J. B., Dudhia J., Gill D. O., Barker D. M., Duda M., et al. (2008). A description of the advanced research WRF version 3 (NCAR Technical Note NCAR/TN-475+STR). p. 113. https://doi.org/10.5065/D68S4MVH