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Yudong Tian
,
Christa D. Peters-Lidard
, and
John B. Eylander

Abstract

A new approach to reduce biases in satellite-based estimates in real time is proposed and tested in this study. Currently satellite-based precipitation estimates exhibit considerable biases, and there have been many efforts to reduce these biases by merging surface gauge measurements with satellite-based estimates. Most of these efforts require timely availability of surface gauge measurements. The new proposed approach does not require gauge measurements in real time. Instead, the Bayesian logic is used to establish a statistical relationship between satellite estimates and gauge measurements from recent historical data. Then this relationship is applied to real-time satellite estimates when gauge data are not yet available. This new scheme is tested over the United States with six years of precipitation estimates from two real-time satellite products [i.e., the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research product 3B42RT and the NOAA Climate Prediction Center (CPC) Morphing technique (CMORPH)] and a gauge analysis dataset [i.e., the CPC unified analysis]. The first 4-yr period was used as the training period to establish a satellite–gauge relationship, which was then applied to the last 2 yr as the correction period, during which gauge data were withheld for training but only used for evaluation. This approach showed that satellite biases were reduced by 70%–100% for the summers in the correction period. In addition, even when sparse networks with only 600 or 300 gauges were used during the training period, the biases were still reduced by 60%–80% and 47%–63%, respectively. The results also show a limitation in this approach as it tends to overadjust both light and strong events toward more intermediate rain rates.

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F. Martin Ralph
,
Michael D. Dettinger
,
Mary M. Cairns
,
Thomas J. Galarneau
, and
John Eylander
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Soroosh Sorooshian
,
Amir AghaKouchak
,
Phillip Arkin
,
John Eylander
,
Efi Foufoula-Georgiou
,
Russell Harmon
,
Jan M. H. Hendrickx
,
Bisher Imam
,
Robert Kuligowski
,
Brian Skahill
, and
Gail Skofronick-Jackson

No abstract available.

Full access
Soroosh Sorooshian
,
Amir AghaKouchak
,
Phillip Arkin
,
John Eylander
,
Efi Foufoula-Georgiou
,
Russell Harmon
,
Jan M. H. Hendrickx
,
Bisher Imam
,
Robert Kuligowski
,
Brian Skahill
, and
Gail Skofronick-Jackson

No abstract available.

Full access
Kristi R. Arsenault
,
Shraddhanand Shukla
,
Abheera Hazra
,
Augusto Getirana
,
Amy McNally
,
Sujay V. Kumar
,
Randal D. Koster
,
Christa D. Peters-Lidard
,
Benjamin F. Zaitchik
,
Hamada Badr
,
Hahn Chul Jung
,
Bala Narapusetty
,
Mahdi Navari
,
Shugong Wang
,
David M. Mocko
,
Chris Funk
,
Laura Harrison
,
Gregory J. Husak
,
Alkhalil Adoum
,
Gideon Galu
,
Tamuka Magadzire
,
Jeanne Roningen
,
Michael Shaw
,
John Eylander
,
Karim Bergaoui
,
Rachael A. McDonnell
, and
James P. Verdin

Abstract

Many regions in Africa and the Middle East are vulnerable to drought and to water and food insecurity, motivating agency efforts such as the U.S. Agency for International Development’s (USAID) Famine Early Warning Systems Network (FEWS NET) to provide early warning of drought events in the region. Each year these warnings guide life-saving assistance that reaches millions of people. A new NASA multimodel, remote sensing–based hydrological forecasting and analysis system, NHyFAS, has been developed to support such efforts by improving the FEWS NET’s current early warning capabilities. NHyFAS derives its skill from two sources: (i) accurate initial conditions, as produced by an offline land modeling system through the application and/or assimilation of various satellite data (precipitation, soil moisture, and terrestrial water storage), and (ii) meteorological forcing data during the forecast period as produced by a state-of-the-art ocean–land–atmosphere forecast system. The land modeling framework used is the Land Information System (LIS), which employs a suite of land surface models, allowing multimodel ensembles and multiple data assimilation strategies to better estimate land surface conditions. An evaluation of NHyFAS shows that its 1–5-month hindcasts successfully capture known historic drought events, and it has improved skill over benchmark-type hindcasts. The system also benefits from strong collaboration with end-user partners in Africa and the Middle East, who provide insights on strategies to formulate and communicate early warning indicators to water and food security communities. The additional lead time provided by this system will increase the speed, accuracy, and efficacy of humanitarian disaster relief, helping to save lives and livelihoods.

Free access
Kristi R. Arsenault
,
Shraddhanand Shukla
,
Abheera Hazra
,
Augusto Getirana
,
Amy McNally
,
Sujay V. Kumar
,
Randal D. Koster
,
Christa D. Peters-Lidard
,
Benjamin F. Zaitchik
,
Hamada Badr
,
Hahn Chul Jung
,
Bala Narapusetty
,
Mahdi Navari
,
Shugong Wang
,
David M. Mocko
,
Chris Funk
,
Laura Harrison
,
Gregory J. Husak
,
Alkhalil Adoum
,
Gideon Galu
,
Tamuka Magadzire
,
Jeanne Roningen
,
Michael Shaw
,
John Eylander
,
Karim Bergaoui
,
Rachael A. McDonnell
, and
James P. Verdin
Full access