scholarly journals Integrating Remote-Sensing and Assimilation Data to Improve Air Temperature on Hot Weather in East China

2021 ◽  
Vol 13 (17) ◽  
pp. 3409
Author(s):  
Suosuo Li ◽  
Yuanpu Liu ◽  
Yongjie Pan ◽  
Zhe Li ◽  
Shihua Lyu
Keyword(s):  
Heat Flux ◽  
Soil Moisture ◽  
Spatial Resolution ◽  
Air Temperature ◽  
Land Surface ◽  
Sensible Heat Flux ◽  
East China ◽  
Area Index ◽  
Near Surface ◽  
Hot Weather

Land-surface characteristics (LSCs) and land-soil moisture conditions can modulate energy partition at the land surface, impact near-surface atmosphere conditions, and further affect land–atmosphere interactions. This study investigates the effect of land-surface-characteristic parameters (LSCPs) including albedo, leaf-area index (LAI), and soil moisture (SM) on hot weather by in East China using the numerical model. Simulations using the Weather Research and Forecasting (WRF) Model were conducted for a hot weather event with a high spatial resolution of 1 km in domain 3 by using ERA-Interim forcing fields on 20 July 2017 until 16:00 UTC on 25 July 2017. The satellite-based albedo and LAI, and assimilation-based soil-moisture data of high temporal–spatial resolution, which are more accurate to match fine weather forecasts and high-resolution simulations, were used to update the default LSCPs. A control simulation with the default LSCPs (WRF_CTL), a main sensitivity simulation with the updated LSCP albedo, LAI and SM (WRF_CHAR), and a series of other sensitivity simulations with one or two updated LSCPs were performed. Results show that WRF_CTL could reproduce the spatial distribution of hot weather, but overestimated air temperature (Ta) and maximal air temperature (Tamax) with a warming bias of 1.05 and 1.32 °C, respectively. However, the WRF_CHAR simulation reduced the warming bias, and improved the simulated Ta and Tamax with reducing relative biases of 33.08% and 29.24%, respectively. Compared to the WRF_CTL, WRF_CHAR presented a negative sensible heat-flux difference, positive latent heat flux, and net radiation difference of the area average. LSCPs modulated the partition of available land-surface energy and then changed the air temperature. On the basis of statistical-correlation analysis, the soil moisture of the top 10 cm is the main factor to improve warming bias on hot weather in East China.

scholarly journals Evaluation and Comparison of Noah and Pleim–Xiu Land Surface Models in MM5 Using GÖTE2001 Data: Spatial and Temporal Variations in Near-Surface Air Temperature

2007 ◽  
Vol 46 (10) ◽  
pp. 1587-1605 ◽  
Author(s):  
J-F. Miao ◽  
D. Chen ◽  
K. Borne
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Model Performance ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Surface Models ◽  
Urban Heat ◽  
Soil Moisture Initialization ◽  
Evident Difference

Abstract In this study, the performance of two advanced land surface models (LSMs; Noah LSM and Pleim–Xiu LSM) coupled with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), version 3.7.2, in simulating the near-surface air temperature in the greater Göteborg area in Sweden is evaluated and compared using the GÖTE2001 field campaign data. Further, the effects of different planetary boundary layer schemes [Eta and Medium-Range Forecast (MRF) PBLs] for Noah LSM and soil moisture initialization approaches for Pleim–Xiu LSM are investigated. The investigation focuses on the evaluation and comparison of diurnal cycle intensity and maximum and minimum temperatures, as well as the urban heat island during the daytime and nighttime under the clear-sky and cloudy/rainy weather conditions for different experimental schemes. The results indicate that 1) there is an evident difference between Noah LSM and Pleim–Xiu LSM in simulating the near-surface air temperature, especially in the modeled urban heat island; 2) there is no evident difference in the model performance between the Eta PBL and MRF PBL coupled with the Noah LSM; and 3) soil moisture initialization is of crucial importance for model performance in the Pleim–Xiu LSM. In addition, owing to the recent release of MM5, version 3.7.3, some experiments done with version 3.7.2 were repeated to reveal the effects of the modifications in the Noah LSM and Pleim–Xiu LSM. The modification to longwave radiation parameterizations in Noah LSM significantly improves model performance while the adjustment of emissivity, one of the vegetation properties, affects Pleim–Xiu LSM performance to a larger extent. The study suggests that improvements both in Noah LSM physics and in Pleim–Xiu LSM initialization of soil moisture and parameterization of vegetation properties are important.

scholarly journals Toward High-Resolution Soil Moisture Monitoring by Combining Active-Passive Microwave and Optical Vegetation Remote Sensing Products with Land Surface Model

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3924 ◽  
Author(s):  
Toride ◽  
Sawada ◽  
Aida ◽  
Koike
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Low Cost ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Parameters ◽  
Area Index ◽  
Coarse Resolution ◽  
Fine Resolution

The assimilation of radiometer and synthetic aperture radar (SAR) data is a promising recent technique to downscale soil moisture products, yet it requires land surface parameters and meteorological forcing data at a high spatial resolution. In this study, we propose a new downscaling approach, named integrated passive and active downscaling (I-PAD), to achieve high spatial and temporal resolution soil moisture datasets over regions without detailed soil data. The Advanced Microwave Scanning Radiometer (AMSR-E) and Phased Array-type L-band SAR (PALSAR) data are combined through a dual-pass land data assimilation system to obtain soil moisture at 1 km resolution. In the first step, fine resolution model parameters are optimized based on fine resolution PALSAR soil moisture and moderate-resolution imaging spectroradiometer (MODIS) leaf area index data, and coarse resolution AMSR-E brightness temperature data. Then, the 25 km AMSR-E observations are assimilated into a land surface model at 1 km resolution with a simple but computationally low-cost algorithm that considers the spatial resolution difference. Precipitation data are used as the only inputs from ground measurements. The evaluations at the two lightly vegetated sites in Mongolia and the Little Washita basin show that the time series of soil moisture are improved at most of the observation by the assimilation scheme. The analyses reveal that I-PAD can capture overall spatial trends of soil moisture within the coarse resolution radiometer footprints, demonstrating the potential of the algorithm to be applied over data-sparse regions. The capability and limitation are discussed based on the simple optimization and assimilation schemes used in the algorithm.

scholarly journals Improved parameterization of snow albedo in WRF + Noah. Part II: Applicability to snow estimates for the Tibetan Plateau

2021 ◽  
Author(s):  
Lian Liu ◽  
Yaoming Ma ◽  
Massimo Menenti ◽  
Rongmingzhu Su ◽  
Nan Yao ◽  
...  
Keyword(s):  
Heat Flux ◽  
Tibetan Plateau ◽  
Air Temperature ◽  
Land Surface ◽  
Snow Depth ◽  
Sensible Heat Flux ◽  
Parameterization Scheme ◽  
The Tibetan Plateau ◽  
Sensible Heat ◽  
Snow Albedo

Abstract. Snow albedo is important to the land surface energy balance and to the water cycle. During snowfall and subsequent snowmelt, snow albedo is usually parameterized as functions of snow related variables in land surface models. However, the default snow albedo scheme in the widely used Noah land surface model shows evident shortcomings in land-atmosphere interactions estimates during snow events on the Tibetan Plateau. Here, we demonstrate that our improved snow albedo scheme performs well after including snow depth as an additional factor. By coupling the WRF and Noah models, this study comprehensively evaluates the performance of the improved snow albedo scheme in simulating eight snow events on the Tibetan Plateau. The modeling results are compared with WRF run with the default Noah scheme and in situ observations. The improved snow albedo scheme significantly outperforms the default Noah scheme in relation to air temperature, albedo and sensible heat flux estimates, by alleviating cold bias estimates, albedo overestimates and sensible heat flux underestimates, respectively. This in turn contributes to more accurate reproductions of snow event evolution. The averaged RMSE relative reductions (and relative increase in correlation coefficients) for air temperature, albedo, sensible heat flux and snow depth reach 27 % (5 %), 32 % (69 %), 13 % (17 %) and 21 % (108 %) respectively. These results demonstrate the strong potential of our improved snow albedo parameterization scheme for snow event simulations on the Tibetan Plateau. Our study provides a theoretical reference for researchers committed to further improving the snow albedo parameterization scheme.

scholarly journals Realistic Initialization of Land Surface States: Impacts on Subseasonal Forecast Skill

2004 ◽  
Vol 5 (6) ◽  
pp. 1049-1063 ◽  
Author(s):  
Randal D. Koster ◽  
Max J. Suarez ◽  
Ping Liu ◽  
Urszula Jambor ◽  
Aaron Berg ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
General Circulation ◽  
Surface States ◽  
Forecast Skill ◽  
Precipitation Forecast ◽  
Surface Model ◽  
Monthly Precipitation ◽  
Near Surface

Abstract Forcing a land surface model (LSM) offline with realistic global fields of precipitation, radiation, and near-surface meteorology produces realistic fields (within the context of the LSM) of soil moisture, temperature, and other land surface states. These fields can be used as initial conditions for precipitation and temperature forecasts with an atmospheric general circulation model (AGCM). Their usefulness is tested in this regard by performing retrospective 1-month forecasts (for May through September, 1979–93) with the NASA Global Modeling and Assimilation Office (GMAO) seasonal prediction system. The 75 separate forecasts provide an adequate statistical basis for quantifying improvements in forecast skill associated with land initialization. Evaluation of skill is focused on the Great Plains of North America, a region with both a reliable land initialization and an ability of soil moisture conditions to overwhelm atmospheric chaos in the evolution of the meteorological fields. The land initialization does cause a small but statistically significant improvement in precipitation and air temperature forecasts in this region. For precipitation, the increases in forecast skill appear strongest in May through July, whereas for air temperature, they are largest in August and September. The joint initialization of land and atmospheric variables is considered in a supplemental series of ensemble monthly forecasts. Potential predictability from atmospheric initialization dominates over that from land initialization during the first 2 weeks of the forecast, whereas during the final 2 weeks, the relative contributions from the two sources are of the same order. Both land and atmospheric initialization contribute independently to the actual skill of the monthly temperature forecast, with the greatest skill derived from the initialization of both. Land initialization appears to contribute the most to monthly precipitation forecast skill.

scholarly journals Estimating Spatial Variability in Atmospheric Properties over Remotely Sensed Land Surface Conditions

2008 ◽  
Vol 47 (8) ◽  
pp. 2147-2165 ◽  
Author(s):  
Giacomo Bertoldi ◽  
William P. Kustas ◽  
John D. Albertson
Keyword(s):  
Heat Flux ◽  
Land Surface ◽  
Sensible Heat Flux ◽  
Remotely Sensed ◽  
Agricultural Landscapes ◽  
Specific Humidity ◽  
General Linear ◽  
Weather Station ◽  
Near Surface ◽  
Flux Estimation

Abstract This paper investigates the spatial relationships between surface fluxes and near-surface atmospheric properties (AP), and the potential errors in flux estimation due to homogeneous atmospheric inputs over heterogeneous landscapes. A large-eddy simulation (LES) model is coupled to a surface energy balance scheme with remotely sensed surface temperature Ts as a key boundary condition. Simulations were performed for different agricultural regions having major contrasts in Ts, canopy cover, and surface roughness z0 between vegetated/irrigated and bare soil areas. If AP from a single weather station in a nonrepresentative location within the landscape are applied uniformly over the domain, significant differences in surface flux estimation with respect to the LES output are observed. The spatial correlations of AP with the fluxes, the land cover properties, and surface states were examined and the spatial scaling of these fields is analyzed using a two-dimensional wavelet technique. The results indicate a significant local correlation of the spatial distributions of the air temperature Ta with the sensible heat flux H, the specific humidity q with the latent heat flux LE, and the wind speed U with z0. These relationships can be described by a general linear form, suggesting that a simple regression relation may be applicable for most agricultural landscapes to estimate spatially variable AP fields. A simple yet practical method is proposed using remotely sensed observations and the land surface scheme, based on general linear expressions derived between Ta and H, q and LE, and U and z0. The method is shown to reproduce the main spatial patterns of AP and to reduce potential errors in local and regionally averaged heat flux estimation. This approach is recommended when only local weather station observations are available.

scholarly journals Sensible heat flux estimated from one-level air temperature near the land surface

2004 ◽  
Vol 31 (7) ◽  
pp. n/a-n/a ◽  
Author(s):  
Jean Fitzmaurice ◽  
Jingfeng Wang ◽  
Rafael L. Bras
Keyword(s):  
Heat Flux ◽  
Air Temperature ◽  
Land Surface ◽  
Sensible Heat Flux ◽  
Sensible Heat

scholarly journals Actual daily evapotranspiration estimated from MERIS and AATSR data over the Chinese Loess Plateau

2009 ◽  
Vol 6 (1) ◽  
pp. 921-942
Author(s):  
R. Liu ◽  
J. Wen ◽  
X. Wang ◽  
L. Wang ◽  
H. Tian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Loess Plateau ◽  
Land Surface ◽  
Sunshine Duration ◽  
Sensible Heat Flux ◽  
Remote Sensing Data ◽  
Chinese Loess Plateau ◽  
Near Surface ◽  
Sensing Data

Abstract. The Loess Plateau is located in north of China and has a significant impact on the climate and ecosystem evolvement over the East Asian continent. Based on the land surface energy balance theory, the potential of using Medium Resolution Imaging Spectrometer (onboard sensor of the Environmental Satellite) remote sensing data on 7, 11 and 27 June 2005 is explored. The "split-window" algorithm is used to retrieve surface temperature from the Advanced the Along-Track Scanning Radiometer, another onboard senor of the Environmental Satellite. Then the near surface net radiation, sensible heat flux and soil heat flux are estimated by using the developed algorithm. We introduce a simple algorithm to predict the heat flux partitioning between the soil and vegetation. Combining the sunshine hours, air temperature, sunshine duration and wind speed measured by weather stations, a model for estimating daily ET is proposed. The instantaneous ET is also converted to daily value. Comparison of latent heats flux retrieved by remote sensing data with ground observation from eddy covariance flux system during Loess Plateau land surface process field Experiment, the maximum and minimum error of this approach are 10.96% and 4.80% respectively, the cause of the bias is also explored and discussed.

scholarly journals Improved parameterization of snow albedo in Noah coupled with Weather Research and Forecasting: applicability to snow estimates for the Tibetan Plateau

2021 ◽  
Vol 25 (9) ◽  
pp. 4967-4981
Author(s):  
Lian Liu ◽  
Yaoming Ma ◽  
Massimo Menenti ◽  
Rongmingzhu Su ◽  
Nan Yao ◽  
...  
Keyword(s):  
Heat Flux ◽  
Tibetan Plateau ◽  
Air Temperature ◽  
Land Surface ◽  
Snow Depth ◽  
Sensible Heat Flux ◽  
Weather Research And Forecasting ◽  
The Tibetan Plateau ◽  
Sensible Heat ◽  
Snow Albedo

Abstract. Snow albedo is important to the land surface energy balance and to the water cycle. During snowfall and subsequent snowmelt, snow albedo is usually parameterized as functions of snow-related variables in land surface models. However, the default snow albedo scheme in the widely used Noah land surface model shows evident shortcomings in land–atmosphere interaction estimates during snow events on the Tibetan Plateau. Here, we demonstrate that our improved snow albedo scheme performs well after including snow depth as an additional factor. By coupling the Weather Research and Forecasting (WRF) and Noah models, this study comprehensively evaluates the performance of the improved snow albedo scheme in simulating eight snow events on the Tibetan Plateau. The modeling results are compared with WRF run with the default Noah scheme and in situ observations. The improved snow albedo scheme significantly outperforms the default Noah scheme in relation to air temperature, albedo and sensible heat flux estimates by alleviating cold bias estimates, albedo overestimates and sensible heat flux underestimates, respectively. This in turn contributes to more accurate reproductions of snow event evolution. The averaged root mean square error (RMSE) relative reductions (and relative increase in correlation coefficients) for air temperature, albedo, sensible heat flux and snow depth reach 27 % (5 %), 32 % (69 %), 13 % (17 %) and 21 % (108 %), respectively. These results demonstrate the strong potential of our improved snow albedo parameterization scheme for snow event simulations on the Tibetan Plateau. Our study provides a theoretical reference for researchers committed to further improving the snow albedo parameterization scheme.

scholarly journals Evaluation of the Soil, Vegetation, and Snow (SVS) Land Surface Model for the Simulation of Surface Energy Fluxes and Soil Moisture under Snow-Free Conditions

Atmosphere ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 278 ◽  
Author(s):  
Gonzalo Leonardini ◽  
François Anctil ◽  
Maria Abrahamowicz ◽  
Étienne Gaborit ◽  
Vincent Vionnet ◽  
...  
Keyword(s):  
Heat Flux ◽  
Soil Moisture ◽  
Land Surface ◽  
Sensible Heat Flux ◽  
Land Surface Model ◽  
Soil Heat Flux ◽  
Heat Fluxes ◽  
Surface Model ◽  
Point Scale ◽  
Energy Fluxes

The recently developed Soil, Vegetation, and Snow (SVS) land surface model is being progressively implemented at Environment and Climate Change Canada (ECCC) for operational numerical weather and hydrological predictions. The objective of this study is to evaluate the ability of SVS, in offline point-scale mode and under snow-free conditions, to simulate the surface heat fluxes and soil moisture when compared to flux tower observations and simulations from the Canadian Land Surface Scheme (CLASS), used here as a benchmark model. To do this, we performed point-scale simulations of between 4 and 12 years of data records at six selected sites of the FLUXNET network under arid, Mediterranean and tropical climates. At all sites, SVS shows realistic simulations of latent heat flux, sensible heat flux and net radiation. Soil heat flux is reasonably well simulated for the arid sites and one Mediterranean site and poorly simulated for the tropical sites. On the other hand, surface soil moisture was reasonably well simulated at the arid and Mediterranean sites and poorly simulated at the tropical sites. SVS performance was comparable to CLASS not only for energy fluxes and soil moisture, but also for more specific processes such as evapotranspiration and water balance.

Sign in / Sign up

Export Citation Format

Share Document