scholarly journals Improving Soil Moisture and Surface Turbulent Heat Flux Estimates by Assimilation of SMAP Brightness Temperatures or Soil Moisture Retrievals and GOES Land Surface Temperature Retrievals

2020 ◽  
Vol 21 (2) ◽  
pp. 183-203 ◽  
Author(s):  
Yang Lu ◽  
Susan C. Steele-Dunne ◽  
Gabriëlle J. M. De Lannoy
Keyword(s):  
Heat Flux ◽  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Surface Heat Flux ◽  
Surface Heat ◽  
Heat Fluxes ◽  
Transfer Model ◽  
Turbulent Heat

AbstractSurface heat fluxes are vital to hydrological and environmental studies, but mapping them accurately over a large area remains a problem. In this study, brightness temperature (TB) observations or soil moisture retrievals from the NASA Soil Moisture Active Passive (SMAP) mission and land surface temperature (LST) product from the Geostationary Operational Environmental Satellite (GOES) are assimilated together into a coupled water and heat transfer model to improve surface heat flux estimates. A particle filter is used to assimilate SMAP data, while a particle smoothing method is adopted to assimilate GOES LST time series, correcting for both systematic biases via parameter updating and for short-term error via state updating. One experiment assimilates SMAP TB at horizontal polarization and GOES LST, a second experiment assimilates SMAP TB at vertical polarization and GOES LST, and a third experiment assimilates SMAP soil moisture retrievals along with GOES LST. The aim is to examine if the assimilation of physically consistent TB and LST observations could yield improved surface heat flux estimates. It is demonstrated that all three assimilation experiments improved flux estimates compared to a no-assimilation case. Assimilating TB data tends to produce smaller bias in soil moisture estimates compared to assimilating soil moisture retrievals, but the estimates are influenced by the respective bias correction approaches. Despite the differences in soil moisture estimates, the flux estimates from different assimilation experiments are in general very similar.

scholarly journals Simulation of Land Surface Climate over China with RegCM4.5: Verification and Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Minghao Yang ◽  
Ruiting Zuo ◽  
Liqiong Wang ◽  
Xiong Chen
Keyword(s):  
Heat Flux ◽  
Soil Moisture ◽  
Surface Temperature ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Land Surface Temperature ◽  
South China ◽  
Land Surface ◽  
Reanalysis Data ◽  
Surface Evaporation

The ability of RegCM4.5 using land surface scheme CLM4.5 to simulate the physical variables related to land surface state was investigated. The NCEP-NCAR reanalysis data for the period 1964–2003 were used to drive RegCM4.5 to simulate the land surface temperature, precipitation, soil moisture, latent heat flux, and surface evaporation. Based on observations and reanalysis data, a few land surface variables were analyzed over China. The results showed that some seasonal features of land surface temperature in summer and winter as well as its magnitude could be simulated well. The simulation of precipitation was sensitive to region and season. The model could, to a certain degree, simulate the seasonal migration of rainband in East China. The overall spatial distribution of the simulated soil moisture was better in winter than in summer. The simulation of latent heat flux was also better in winter. In summer, the latent heat flux bias mainly arose from surface evaporation bias in Northwest China, and it primarily arose from vegetation evapotranspiration bias in South China. In addition, the large latent heat flux bias in South China during summer was probably due to less precipitation generated in the model and poor representation of vegetation cover in this region.

scholarly journals Impact of Soil Moisture Data Resolution on Soil Moisture and Surface Heat Flux Estimates through Data Assimilation: A Case Study in the Southern Great Plains

2019 ◽  
Vol 20 (4) ◽  
pp. 715-730 ◽  
Author(s):  
Yang Lu ◽  
Jianzhi Dong ◽  
Susan C. Steele-Dunne
Keyword(s):  
Heat Flux ◽  
Soil Moisture ◽  
Spatial Resolution ◽  
Surface Heat Flux ◽  
Root Zone ◽  
Sampling Frequency ◽  
Surface Heat ◽  
Heat Fluxes ◽  
Soil Moisture Data ◽  
Temporal Sampling

Abstract The spatial heterogeneity and temporal variation of soil moisture and surface heat fluxes are key to many geophysical and environmental studies. It has been demonstrated that they can be mapped by assimilating soil thermal and wetness information into surface energy balance models. The aim of this work is to determine whether enhancing the spatial resolution or temporal sampling frequency of soil moisture data could improve soil moisture or surface heat flux estimates. Two experiments are conducted in an area mainly covered by grassland, and land surface temperature (LST) observations from the Geostationary Operational Environmental Satellite (GOES) mission are assimilated together with either an enhanced L-band passive soil moisture product (9 km, 2–3 days) from the Soil Moisture Active Passive (SMAP) mission or a merged product (36 km, quasi-daily) from the SMAP and the Soil Moisture Ocean Salinity (SMOS) mission. The results suggest that the availability of soil moisture observations is increased by 41% after merging data from the SMAP and the SMOS missions. A comparison with results from a previous study that assimilated a coarser SMAP soil moisture product (36 km, 2–3 days) suggests that enhancing the temporal sampling frequency of soil moisture observations leads to improved soil moisture estimates at both the surface and root zone, and the largest improvement is seen in the bias metric (0.008 and 0.007 m3 m−3 on average at the surface and root zone, respectively). Enhancing the spatial resolution, however, does not significantly improve soil moisture estimates, particularly at the surface. Surface heat flux estimates from assimilating soil moisture data of different spatial or temporal resolutions are very similar.

scholarly journals Improving Predictions of Water and Heat Fluxes by Assimilating MODIS Land Surface Temperature Products into the Common Land Model

2011 ◽  
Vol 12 (2) ◽  
pp. 227-244 ◽  
Author(s):  
Tongren Xu ◽  
Shaomin Liu ◽  
Shunlin Liang ◽  
Jun Qin
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Heat Fluxes ◽  
Model Parameters ◽  
Modis Land Surface Temperature ◽  
Common Land Model ◽  
The Common

Abstract Four data assimilation scheme combinations derived from two strategies and two optimization algorithms [the ensemble Kalman filter (EnKF) and the shuffled complex evolution method developed at The University of Arizona (SCE-UA)] are developed based on the Common Land Model (CLM) to improve predictions of water and heat fluxes. The first strategy is constructed through adjusting the soil temperature, while the second strategy adjusts the soil moisture. Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products are compared with ground-measured surface temperature, and assimilated into the CLM. The relationship equation between the MODIS LST products and CLM surface temperature is taken as the observation operator and the root-mean-square error (RMSE) is applied as the observation error. The assimilation results are validated by measurements from six observation sites located in Germany, the United States, and China. Results indicate that the developed data assimilation schemes can improve estimates of water and heat fluxes. Overall, strategy 2 is superior to strategy 1 when using the same optimization algorithm. The EnKF algorithm performs slightly better than the SCE-UA algorithm when using the same strategy. Strategy 2 combined with the EnKF algorithm performs best for water and heat fluxes, and the reductions in the RMSE are found to be 24.0 and 15.2 W m−2 for sensible and latent heat fluxes, respectively. The joint assimilation of the MODIS LST and soil moisture observations can produce better results for strategy 2 with the SCE-UA. Since preprocessing model parameters are used in this study, the uncertainties in the model parameters may have resulted in suboptimal assimilation results. Therefore, model calibrations should be conducted in the future.

What balloon soundings can tell about surface heat flux partitioning

2020 ◽  
Author(s):  
Jasper Denissen ◽  
Hendrik Wouters ◽  
René Orth ◽  
Diego Miralles ◽  
Ryan Teuling
Keyword(s):  
Heat Flux ◽  
Soil Moisture ◽  
Land Surface ◽  
Surface Heat Flux ◽  
Sensible Heat Flux ◽  
Surface Heat ◽  
Atmospheric Measurements ◽  
Flux Partitioning ◽  
Temperature And Humidity ◽  
Soil Moisture Availability

<p>The land surface can influence near-surface weather. This happens, amongst others, through the impact of soil moisture availability on surface heat fluxes: when soil moisture is unavailable in soil moisture-limited conditions, most of the available energy will be used for heating the air above the land surface (sensible heat flux). But as soil moisture increases, evapotranspiration (latent heat flux) increases, affecting the surface heat flux partitioning. At the point that ample soil moisture is available in energy-limited conditions, the surface heat flux partitioning remains unaffected by soil moisture. The atmospheric boundary layer (ABL) responds to changes in surface heat flux partitioning in particular in terms of its temperature and humidity. Based on these mechanisms, observations of boundary layer dynamics should allow to infer the large-scale land surface state.</p><p>The goal of this study is to use atmospheric measurements of temperature and humidity to estimate the surface heat flux partitioning. This is achieved by constraining an ABL model (CLASS4GL) with the vertical temperature and humidity profiles as observed by thousands of soundings of hot air balloons across the globe. In CLASS4GL, the initial soil moisture is adjusted to yield matching modelled versus observed vertical temperature and humidity profiles. By doing so, the resulting surface fluxes are inferred exclusively from atmospheric measurements.</p><p>We find that ABL’s tend to higher, warmer and drier in water-limited conditions. This largely results from changes in soil moisture availability, which mainly affects the sensible heat flux and consequently, the surface heat flux partitioning. We determine the critical soil moisture, which distinguishes between soil moisture- and energy- limited conditions, using the ratio between the sensible- and latent heat flux and independent satellite surface soil moisture.</p><p>This is the first time that balloon soundings are used globally to assess the critical soil moisture. This research will help to further improve our understanding of land-atmosphere feedbacks and foster a correct representation of land surface characteristics in Land Models and subsequently, Climate Models.</p>

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