Next Article in Journal
The Spatial–Spectral–Environmental Extraction Endmember Algorithm and Application in the MODIS Fractional Snow Cover Retrieval
Next Article in Special Issue
Improving the Estimation of Weighted Mean Temperature in China Using Machine Learning Methods
Previous Article in Journal
A Semi-Automated Method for Estimating Adélie Penguin Colony Abundance from a Fusion of Multispectral and Thermal Imagery Collected with Unoccupied Aircraft Systems
Previous Article in Special Issue
Evaluation of a Microwave Emissivity Module for Snow Covered Area with CMEM in the ECMWF Integrated Forecasting System
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Met Office Operational Soil Moisture Analysis System

1
Met Office, FitzRoy Road, Exeter, Devon EX1 3PB, UK
2
MetOffice@Reading, Meteorology Building, University of Reading, Reading, Berkshire RG6 6BB, UK
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(22), 3691; https://doi.org/10.3390/rs12223691
Submission received: 30 September 2020 / Revised: 6 November 2020 / Accepted: 9 November 2020 / Published: 11 November 2020
(This article belongs to the Special Issue Remote Sensing of Land Surface and Earth System Modelling)

Abstract

In this study, the current Met Office operational land surface data assimilation system used to produce soil moisture analyses is presented. The main aim of including Land Surface Data Assimilation (LSDA) in both the global and regional systems is to improve forecasts of surface air temperature and humidity. Results from trials assimilating pseudo-observations of 1.5 m air temperature and specific humidity and satellite-derived soil wetness (ASCAT) observations are analysed. The pre-processing of all the observations is described, including the definition and construction of the pseudo-observations. The benefits of using both observations together to produce improved forecasts of surface air temperature and humidity are outlined both in the winter and summer seasons. The benefits of using active LSDA are quantified by the root mean squared error, which is computed using both surface observations and European Centre for Medium-Range Weather Forecasts (ECMWF) analyses as truth. For the global model trials, results are presented separately for the Northern (NH) and Southern (SH) hemispheres. When compared against ground-truth, LSDA in winter NH appears neutral, but in the SH it is the assimilation of ASCAT that contributes to approximately a 2% improvement in temperatures at lead times beyond 48 h. In NH summer, the ASCAT soil wetness observations degrade the forecasts against observations by about 1%, but including the screen level pseudo-observations provides a compensating benefit. In contrast, in the SH, the positive effect comes from including the ASCAT soil wetness observations, and when both observations types are assimilated there is a compensating effect. Finally, we demonstrate substantial improvements to hydrological prediction when using land surface data assimilation in the regional model. Using the Nash-Sutcliffe Efficiency (NSE) metric as an aggregated measure of river flow simulation skill relative to observations, we find that NSE was improved at 106 of 143 UK river gauge locations considered after LSDA was introduced. The number of gauge comparisons where NSE exceeded 0.5 is also increased from 17 to 28 with LSDA.
Keywords: data assimilation; land surface; soil moisture; NWP; integrated hydrology data assimilation; land surface; soil moisture; NWP; integrated hydrology
Graphical Abstract

Share and Cite

MDPI and ACS Style

Gómez, B.; Charlton-Pérez, C.L.; Lewis, H.; Candy, B. The Met Office Operational Soil Moisture Analysis System. Remote Sens. 2020, 12, 3691. https://doi.org/10.3390/rs12223691

AMA Style

Gómez B, Charlton-Pérez CL, Lewis H, Candy B. The Met Office Operational Soil Moisture Analysis System. Remote Sensing. 2020; 12(22):3691. https://doi.org/10.3390/rs12223691

Chicago/Turabian Style

Gómez, Breogán, Cristina L. Charlton-Pérez, Huw Lewis, and Brett Candy. 2020. "The Met Office Operational Soil Moisture Analysis System" Remote Sensing 12, no. 22: 3691. https://doi.org/10.3390/rs12223691

APA Style

Gómez, B., Charlton-Pérez, C. L., Lewis, H., & Candy, B. (2020). The Met Office Operational Soil Moisture Analysis System. Remote Sensing, 12(22), 3691. https://doi.org/10.3390/rs12223691

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop