An Assessment of the Impact of Land Thermal Infrared Observation on Regional Weather Forecasts Using Two Different Data Assimilation Approaches
Abstract
:1. Introduction
2. Semi-Coupled LIS and WRF Data Assimilation System
2.1. Land Information System
2.2. NASA Unified Weather and Research Forecast Model
2.3. LIS Semi-Coupled with WRF
3. Datasets
3.1. LST-Derived Soil Moisture
3.2. GOES Land Surface Temperature
3.3. Ground Weather Observational Data for WFR Forecast Validation
4. Data Assimilation Experiments
4.1. Open-Loop Run of the LIS-WRF System
4.2. LST Data Assimilation
4.3. LST-Based ALEXI SM Data Assimilation
5. Results
5.1. Differences in WRF Forecasts with and without Assimilations
5.2. Evaluation of the WRF Forecasts against Ground Observations
5.2.1. Two-Meter Air Temperature
5.2.2. Two-Meter Relative Humidity
5.2.3. Daily Precipitation
5.2.4. Validation Summary
6. Discussion and Summary
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Variables | Assignment |
---|---|
WRF dynamical core | Advanced Research WRF |
grid spacing/projection | 12 km/Lambert |
dimension (west-east by south-north) | 480 × 400 |
Integration time step | 24 s (the same in LIS and ARW) |
Vertical dimension | 42 |
number of soil levels or layers | 4 |
Land usage | MODIS (20-category) |
Microphysics | 5 (Eta microphysics: the operational microphysics in NCEP models) |
Land surface | Noah (v3.3) |
Planetary boundary layer | Mellor–Yamada–Janjic scheme |
Forecasts | Approaches | Difference in Normalized RMSE * Compared to Open-Loop Run (Percentage) | Number of Sites with Improvements Compared to Open-Loop Run (Percentage) | ||
---|---|---|---|---|---|
TX-NM Region | CONUS | TX-NM Region | CONUS | ||
T-2m | TIR SM DA | 2.65 | 1.58 | 79.58 | 72.29 |
LST DA | 0.68 | −0.97 | 61.26 | 36.09 | |
RH-2m | TIR SM DA | 16.22 | 13.10 | 82.72 | 80.45 |
LST DA | 1.82 | 1.68 | 74.87 | 69.07 | |
Forecasts | Approaches | Difference in Hit rate ** Compared to Open-Loop Run | Difference In RMSE Compared To Open-Loop Run | ||
Precipitation | TIR SM DA | 0.16 | 0.18 | ||
LST DA | 0.08 | 0.04 |
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Fang, L.; Zhan, X.; Hain, C.R.; Yin, J.; Liu, J.; Schull, M.A. An Assessment of the Impact of Land Thermal Infrared Observation on Regional Weather Forecasts Using Two Different Data Assimilation Approaches. Remote Sens. 2018, 10, 625. https://doi.org/10.3390/rs10040625
Fang L, Zhan X, Hain CR, Yin J, Liu J, Schull MA. An Assessment of the Impact of Land Thermal Infrared Observation on Regional Weather Forecasts Using Two Different Data Assimilation Approaches. Remote Sensing. 2018; 10(4):625. https://doi.org/10.3390/rs10040625
Chicago/Turabian StyleFang, Li, Xiwu Zhan, Christopher R. Hain, Jifu Yin, Jicheng Liu, and Mitchell A. Schull. 2018. "An Assessment of the Impact of Land Thermal Infrared Observation on Regional Weather Forecasts Using Two Different Data Assimilation Approaches" Remote Sensing 10, no. 4: 625. https://doi.org/10.3390/rs10040625