The Impact of Satellite‐Derived Land Surface Temperatures on Numerical Weather Prediction Analyses and Forecasts

Candy, Brett; Saunders, Roger; Ghent, Darren; Bulgin, Claire E.

doi:10.1002/2016jd026417

Cited by 21 publications

(25 citation statements)

References 34 publications

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“…Currently, as LSTs are not assimilated into the UM, they provide an independent source of data for assessing the performance of the land surface model's surface exchange and the boundary layer schemes (Edwards, 2010). Recently, trials have been completed at the Met Office to perform the first direct assimilation of night-time satellite LST into operational NWP, and this study has demonstrated improvements in near surface air temperature forecasts and soil temperatures (Candy et al, 2017). The required LST uncertainty for 15 assimilation within the Met Office operational assimilation scheme is less than 2 K, and Candy et al, (2017) highlights the need to further understand the large errors in daytime LST in order to advance NWP data assimilation.…”

Section: Introductionmentioning

confidence: 91%

Evaluating the Met Office Unified Model Global Atmosphere/Land 3.1 (GA/L3.1) and Global Atmosphere/Land 6.1 (GA/L6.1) land surface temperature. Outcomes of the SALSTICE campaign

Brooke

Harlow

Scott

et al. 2018

Preprint

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A limitation of the Met Office operational data assimilation scheme is that surface-sensitive infrared satellite 10 sounding channels cannot be used during daytime periods where Numerical Weather Prediction (NWP) model background land surface temperature (LST) biases are greater than 2 K. The Met Office Unified Model (UM) has a significant cold LST bias in semi-arid regions when compared with satellite observations. UM LST biases were evaluated at global resolution and in a Limited Area Models (LAM) at 2.2 km resolution over the SALSTICE (Semi-Arid Land Surface Temperature and IASI Calibration Experiment) experimental domain in southeastern Arizona. This validation is in conjunction with eddy-covariance 15 flux tower measurements. LST biases in the Global Atmosphere/Land 3.1 (GA/L3.1) configuration were largest in the midmorning with respect to Terra (-13.6+2.8 K at the Kendall Grassland site). The diurnal cycle of LST in Global Atmosphere/Land 6.1 (GA/L6.1) showed a significant improvement relative to GA/L3.1. The higher resolution LAM showed added value over the global configurations.The spatial distribution of the LST biases relative to Moderate Resolution Imaging Spectroradiometer (MODIS) and 20 the modelled bare soil cover fraction were found to be moderately correlated (0.61+0.08) during the daytime, which suggests that regions of cold LST bias are associated with low bare soil cover fraction. Coefficients of correlation with the shrub surface fractions followed the same trend as the bare soil cover fraction although with a less significant correlation (0.36+0.09), and indicate that the sparse vegetation canopies in southeastern Arizona are not well represented in UM ancillary datasets. The x-component of the orographic slope was positively correlated with the LST bias (0.35+0.06) and identified that regions 25 of cold model LST bias are found on easterly slopes and regions of warm model LST bias are found on westerly slopes. An overestimate in the modelled turbulent heat and moisture fluxes at the eddy-covariance flux sites was found to be coincident with an underestimate in the ground heat flux.Geosci. Model Dev. Discuss., https://doi.

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Section: Introductionmentioning

confidence: 91%

Evaluating the Met Office Unified Model Global Atmosphere/Land 3.1 (GA/L3.1) and Global Atmosphere/Land 6.1 (GA/L6.1) land surface temperature. Outcomes of the SALSTICE campaign

Brooke

Harlow

Scott

et al. 2018

Preprint

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“…Recent studies have considered assimilating satellite derived LSTs over land. In fact, assimilating geostationary satellites derived LSTs in a land surface model with nighttime observations [7] have shown encouraging results at global scale both for the surface scheme and the atmospheric forecasts. Moreover, the assimilation of polar-orbiting satellite retrieved LSTs, when satellite data are available near the peak of diurnal LST, considerably improved the estimation of the fields of energy balance components and surface control on evaporation [8].…”

Section: Introductionmentioning

confidence: 99%

Use of Infrared Satellite Observations for the Surface Temperature Retrieval over Land in a NWP Context

et al. 2019

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In Numerical Weather Prediction (NWP), an accurate description of surface temperature is needed to assimilate satellite observations. These observations produced by infrared and microwave sensors, help retrieving good quality land surface temperature (LST) by using surface sensitive channels and emissivity atlases. This work is a preparatory step in order to assimilate LSTs in Météo-France NWP models surface analysis. We focus on IASI and SEVIRI sensors. The first part of this work aims at comparing the SEVIRI retrieved LST to local observations from two stations included in the meso-scale AROME-France domain over four periods from different seasons. Diurnal cycles of local LST and SEVIRI LST show a good agreement especially for the summer period. Averaged biases show seasonal variability and are smaller during Winter and Autumn with less than 1 K values for both stations. The second part of the study deals with the comparison of LST values retrieved from different infrared sensors in AROME-France model. First results show encouraging agreement between both LSTs. A comparison during Autumn period for clear sky conditions reveals an almost null bias and a standard deviation of about 1.6 K. More detailed comparisons were performed over contrasted seasons with a special attention to diurnal cycles for both sensors. A better agreement is noticed during nighttime. The last step of this inter-comparison was to study the simulation of SEVIRI and IASI brightness temperatures by using a fast radiative transfer model. Thus, several simulations have been run covering various dates from different seasons by daytime and nighttime using SEVIRI LSTs, IASI LSTs and AROME-France model LSTs. Simulated brightness temperatures were then compared to observations. As expected, the best simulations are the ones using the LST retrieved from the sensor for which simulations are performed. However, the LST retrieved from another sensor provides better simulations than the predicted LST from the model especially during nighttime. For IASI simulations, SEVIRI LSTs increase RMSE by 0.2 K to 0.9 K compared to IASI LSTs for nighttime case and by around 1.5 K for daytime.

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“…Parameters such as soil moisture, surface temperature, and the rate of surface precipitation are important meteorological factors [1][2][3][4]. However, observations for those parameters when obtained using traditional direct observing methods are often limited by the coverage of the observation stations [5].…”

Section: Introductionmentioning

confidence: 99%

Detection and Analysis of C-Band Radio Frequency Interference in AMSR2 Data over Land

Qian

Bao

et al. 2019

Remote Sensing

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A simplified generalized radio frequency interference (RFI) detection method and principal component analysis (PCA) method are utilized to detect and attribute the sources of C-band RFI in AMSR2 L1 brightness temperature data over land during 1–16 July 2017. The results show that the consistency between the two methods provides confidence that RFI may be reliably detected using either of the methods, and the only difference is that the scope of the RFI-contaminated area identified by the former algorithm is larger in some areas than that using the latter method. Strong RFI signals at 6.925 GHz are mainly distributed in the United States, Japan, India, Brazil, and some parts of Europe; meanwhile, RFI signals at 7.3 GHz are mainly distributed in Latin America, Asia, Southern Europe, and Africa. However, no obvious 7.3 GHz RFI appears in the United States or India, indicating that the 7.3 GHz channels mitigate the effects of the C-band RFI in these regions. The RFI signals whose position does not vary with the Earth azimuth of the observations generally come from stable, continuous sources of active ground-based microwave radiation, while the RFI signals which are observed only in some directions on a kind of scanning orbit (ascending/descending) mostly arise from reflected geostationary satellite signals.

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The Impact of Satellite‐Derived Land Surface Temperatures on Numerical Weather Prediction Analyses and Forecasts

Cited by 21 publications

References 34 publications

Evaluating the Met Office Unified Model Global Atmosphere/Land 3.1 (GA/L3.1) and Global Atmosphere/Land 6.1 (GA/L6.1) land surface temperature. Outcomes of the SALSTICE campaign

Evaluating the Met Office Unified Model Global Atmosphere/Land 3.1 (GA/L3.1) and Global Atmosphere/Land 6.1 (GA/L6.1) land surface temperature. Outcomes of the SALSTICE campaign

Use of Infrared Satellite Observations for the Surface Temperature Retrieval over Land in a NWP Context

Detection and Analysis of C-Band Radio Frequency Interference in AMSR2 Data over Land

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