A preliminary assessment of the impact of SMAP Soil Moisture on numerical weather Forecasts from GFS and NUWRF models

Xiwu Zhan; Weizhong Zheng; Li Fang; Jicheng Liu; Chris Hain; Jifu Yin; Michael Ek

doi:10.1109/IGARSS.2016.7730362

A preliminary assessment of the impact of SMAP Soil Moisture on numerical weather Forecasts from GFS and NUWRF models

Xiwu Zhan
, Weizhong Zheng
, Li Fang
, Jicheng Liu
, Chris Hain
, Jifu Yin
, Michael Ek

NOAA NESDIS Center for Satellite Applications and Research
NOAA NWS National Centers for Environmental Predictions
UMD-ESSIC Cooperate Institute of Climate Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

16 Scopus citations

Abstract

NASA Soil Moisture Active/Passive (SMAP) satellite was launched on January 31^st, 2015 and has been providing global soil moisture (SM) data products since April 2015. One of the primary justifications of the mission was to improve numerical weather predictions. With the SMAP SM data becoming available, it is anxiously expected that SMAP SM data could be demonstrated to significantly improve weather forecasts from numerical weather prediction (NWP) models. In this study, the NOAA Global Forecast System (GFS) and NASA Unified Weather Research and Forecast (NUWRF) model coupled with NASA Land Information System are used to carry out the demonstration. A hardwired Ensemble Kalman filter is implemented within GFS to assimilate surface SM observations. For assimilating SM data into NUWRF model, NASA Land Information System (LIS) is coupled with the NUWRF model. In this paper preliminary results of SMAP soil moisture impact on GFS and NUWRF forecasts are presented after the assimilation algorithms and system designs are introduced. Plans for more comprehensive assessment of the satellite soil moisture data impact on NWP models will be discussed.

Original language	English
Title of host publication	2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5229-5232
Number of pages	4
ISBN (Electronic)	9781509033324
DOIs	https://doi.org/10.1109/IGARSS.2016.7730362
State	Published - Nov 1 2016
Event	36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, China Duration: Jul 10 2016 → Jul 15 2016

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2016-November

Conference

Conference	36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Country/Territory	China
City	Beijing
Period	07/10/16 → 07/15/16

Keywords

GFS
NUWRF
NWP
SMAP
soil moisture

Access to Document

10.1109/IGARSS.2016.7730362

Cite this

Zhan, X., Zheng, W., Fang, L., Liu, J., Hain, C., Yin, J., & Ek, M. (2016). A preliminary assessment of the impact of SMAP Soil Moisture on numerical weather Forecasts from GFS and NUWRF models. In 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings (pp. 5229-5232). Article 7730362 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2016-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2016.7730362

Zhan, Xiwu ; Zheng, Weizhong ; Fang, Li et al. / A preliminary assessment of the impact of SMAP Soil Moisture on numerical weather Forecasts from GFS and NUWRF models. 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 5229-5232 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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title = "A preliminary assessment of the impact of SMAP Soil Moisture on numerical weather Forecasts from GFS and NUWRF models",

abstract = "NASA Soil Moisture Active/Passive (SMAP) satellite was launched on January 31st, 2015 and has been providing global soil moisture (SM) data products since April 2015. One of the primary justifications of the mission was to improve numerical weather predictions. With the SMAP SM data becoming available, it is anxiously expected that SMAP SM data could be demonstrated to significantly improve weather forecasts from numerical weather prediction (NWP) models. In this study, the NOAA Global Forecast System (GFS) and NASA Unified Weather Research and Forecast (NUWRF) model coupled with NASA Land Information System are used to carry out the demonstration. A hardwired Ensemble Kalman filter is implemented within GFS to assimilate surface SM observations. For assimilating SM data into NUWRF model, NASA Land Information System (LIS) is coupled with the NUWRF model. In this paper preliminary results of SMAP soil moisture impact on GFS and NUWRF forecasts are presented after the assimilation algorithms and system designs are introduced. Plans for more comprehensive assessment of the satellite soil moisture data impact on NWP models will be discussed.",

keywords = "GFS, NUWRF, NWP, SMAP, soil moisture",

author = "Xiwu Zhan and Weizhong Zheng and Li Fang and Jicheng Liu and Chris Hain and Jifu Yin and Michael Ek",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 ; Conference date: 10-07-2016 Through 15-07-2016",

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Zhan, X, Zheng, W, Fang, L, Liu, J, Hain, C, Yin, J & Ek, M 2016, A preliminary assessment of the impact of SMAP Soil Moisture on numerical weather Forecasts from GFS and NUWRF models. in 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings., 7730362, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2016-November, Institute of Electrical and Electronics Engineers Inc., pp. 5229-5232, 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016, Beijing, China, 07/10/16. https://doi.org/10.1109/IGARSS.2016.7730362

A preliminary assessment of the impact of SMAP Soil Moisture on numerical weather Forecasts from GFS and NUWRF models. / Zhan, Xiwu; Zheng, Weizhong; Fang, Li et al.
2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 5229-5232 7730362 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2016-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Zhan, Xiwu

AU - Zheng, Weizhong

AU - Fang, Li

AU - Liu, Jicheng

AU - Hain, Chris

AU - Yin, Jifu

AU - Ek, Michael

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N2 - NASA Soil Moisture Active/Passive (SMAP) satellite was launched on January 31st, 2015 and has been providing global soil moisture (SM) data products since April 2015. One of the primary justifications of the mission was to improve numerical weather predictions. With the SMAP SM data becoming available, it is anxiously expected that SMAP SM data could be demonstrated to significantly improve weather forecasts from numerical weather prediction (NWP) models. In this study, the NOAA Global Forecast System (GFS) and NASA Unified Weather Research and Forecast (NUWRF) model coupled with NASA Land Information System are used to carry out the demonstration. A hardwired Ensemble Kalman filter is implemented within GFS to assimilate surface SM observations. For assimilating SM data into NUWRF model, NASA Land Information System (LIS) is coupled with the NUWRF model. In this paper preliminary results of SMAP soil moisture impact on GFS and NUWRF forecasts are presented after the assimilation algorithms and system designs are introduced. Plans for more comprehensive assessment of the satellite soil moisture data impact on NWP models will be discussed.

AB - NASA Soil Moisture Active/Passive (SMAP) satellite was launched on January 31st, 2015 and has been providing global soil moisture (SM) data products since April 2015. One of the primary justifications of the mission was to improve numerical weather predictions. With the SMAP SM data becoming available, it is anxiously expected that SMAP SM data could be demonstrated to significantly improve weather forecasts from numerical weather prediction (NWP) models. In this study, the NOAA Global Forecast System (GFS) and NASA Unified Weather Research and Forecast (NUWRF) model coupled with NASA Land Information System are used to carry out the demonstration. A hardwired Ensemble Kalman filter is implemented within GFS to assimilate surface SM observations. For assimilating SM data into NUWRF model, NASA Land Information System (LIS) is coupled with the NUWRF model. In this paper preliminary results of SMAP soil moisture impact on GFS and NUWRF forecasts are presented after the assimilation algorithms and system designs are introduced. Plans for more comprehensive assessment of the satellite soil moisture data impact on NWP models will be discussed.

KW - GFS

KW - NUWRF

KW - NWP

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KW - soil moisture

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M3 - Conference contribution

AN - SCOPUS:85007449461

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 5229

EP - 5232

BT - 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016

Y2 - 10 July 2016 through 15 July 2016

ER -

Zhan X, Zheng W, Fang L, Liu J, Hain C, Yin J et al. A preliminary assessment of the impact of SMAP Soil Moisture on numerical weather Forecasts from GFS and NUWRF models. In 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. p. 5229-5232. 7730362. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2016.7730362

A preliminary assessment of the impact of SMAP Soil Moisture on numerical weather Forecasts from GFS and NUWRF models

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