Retrieval of Hourly Records of Surface Hydrometeorological Variables Using Satellite Remote Sensing Data

2015 ◽  
Vol 16 (1) ◽  
pp. 147-157 ◽  
Author(s):  
Sanaz Moghim ◽  
Andrew Jay Bowen ◽  
Sepideh Sarachi ◽  
Jingfeng Wang
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Sensible Heat Flux ◽  
Surface Air Temperature ◽  
Remote Sensing Data ◽  
Heat Fluxes ◽  
Production Model ◽  
Integral Model ◽  
Near Surface ◽  
Surface Heat Fluxes

Abstract A new algorithm is formulated for retrieving hourly time series of surface hydrometeorological variables including net radiation, sensible heat flux, and near-surface air temperature aided by hourly visible images from the Geostationary Operational Environmental Satellite (GOES) and in situ observations of mean daily air temperature. The algorithm is based on two unconventional, recently developed methods: the maximum entropy production model of surface heat fluxes and the half-order derivative–integral model that has been tested previously. The close agreement between the retrieved hourly variables using remotely sensed input and the corresponding field observations indicates that this algorithm is an effective tool in remote sensing of the earth system.

scholarly journals Integrating Remote Sensing Data with WRF for Improved Simulations of Oasis Effects on Local Weather Processes over an Arid Region in Northwestern China

2012 ◽  
Vol 13 (2) ◽  
pp. 573-587 ◽  
Author(s):  
Xiaohang Wen ◽  
Shihua Lu ◽  
Jiming Jin
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Relative Humidity ◽  
Soil Temperature ◽  
Air Temperature ◽  
Remote Sensing Data ◽  
Heat Fluxes ◽  
Near Surface ◽  
Sensing Data ◽  
Soil Temperature And Moisture

Abstract Land use/cover types derived by satellite remote sensing data from the Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) were used to replace the U.S. Geological Survey (USGS) data in the Weather Research and Forecasting Model (WRF). Simulations in this study were further improved by modifying the initial fields of WRF with soil temperature and moisture observations, because these two variables are important to producing “cold–wet island” effects. A series of WRF simulations were performed to describe microclimate characteristics and the local thermal circulation generated by the inhomogeneous surface over the Jinta oasis, which is located in Gansu—a northwestern province of China. Comparison between simulations and observations showed that the WRF results produced with observed soil temperature and moisture initializations agreed well with near-surface measurements of air temperature, relative humidity, and wind direction. Moreover, low temperatures over the oasis were found to coexist with high temperatures over the bare land, further leading to developments of local atmospheric circulation. The simulated winds over the oasis showed airmass divergence over the surface layer, triggering local circulation in the upper level. The integration of the MODIS land use/cover data with WRF and the initialization of WRF’s soil temperature and moisture with in situ observations improved the simulations in air temperature, relative humidity, and heat fluxes. These improvements enabled the WRF to reproduce the observed “cold and wet island” effects of the oasis.

scholarly journals Biogenic isoprene emissions driven by regional weather predictions using different initialization methods: case studies during the SEAC<sup>4</sup>RS and DISCOVER-AQ airborne campaigns

2017 ◽  
Vol 10 (8) ◽  
pp. 3085-3104 ◽  
Author(s):  
Min Huang ◽  
Gregory R. Carmichael ◽  
James H. Crawford ◽  
Armin Wisthaler ◽  
Xiwu Zhan ◽  
...  
Keyword(s):  
Case Studies ◽  
Air Temperature ◽  
Initial Conditions ◽  
Wrf Model ◽  
Surface Air Temperature ◽  
Heat Fluxes ◽  
Aircraft Observation ◽  
Near Surface ◽  
Boundary Layer Height ◽  
Small Scales

Abstract. Land and atmospheric initial conditions of the Weather Research and Forecasting (WRF) model are often interpolated from a different model output. We perform case studies during NASA's SEAC4RS and DISCOVER-AQ Houston airborne campaigns, demonstrating that using land initial conditions directly downscaled from a coarser resolution dataset led to significant positive biases in the coupled NASA-Unified WRF (NUWRF, version 7) surface and near-surface air temperature and planetary boundary layer height (PBLH) around the Missouri Ozarks and Houston, Texas, as well as poorly partitioned latent and sensible heat fluxes. Replacing land initial conditions with the output from a long-term offline Land Information System (LIS) simulation can effectively reduce the positive biases in NUWRF surface air temperature by ∼ 2 °C. We also show that the LIS land initialization can modify surface air temperature errors almost 10 times as effectively as applying a different atmospheric initialization method. The LIS-NUWRF-based isoprene emission calculations by the Model of Emissions of Gases and Aerosols from Nature (MEGAN, version 2.1) are at least 20 % lower than those computed using the coarser resolution data-initialized NUWRF run, and are closer to aircraft-observation-derived emissions. Higher resolution MEGAN calculations are prone to amplified discrepancies with aircraft-observation-derived emissions on small scales. This is possibly a result of some limitations of MEGAN's parameterization and uncertainty in its inputs on small scales, as well as the representation error and the neglect of horizontal transport in deriving emissions from aircraft data. This study emphasizes the importance of proper land initialization to the coupled atmospheric weather modeling and the follow-on emission modeling. We anticipate it to also be critical to accurately representing other processes included in air quality modeling and chemical data assimilation. Having more confidence in the weather inputs is also beneficial for determining and quantifying the other sources of uncertainties (e.g., parameterization, other input data) of the models that they drive.

scholarly journals Estimating urban heat island effects on near-surface air temperature records of Uccle (Brussels, Belgium): an observational and modeling study

2011 ◽  
Vol 6 (1) ◽  
pp. 27-34 ◽  
Author(s):  
R. Hamdi ◽  
H. Van de Vyver
Keyword(s):  
Remote Sensing ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Heat Island ◽  
Urban Bias ◽  
Near Surface ◽  
Urban Warming ◽  
Urban Heat ◽  
Weather Stations

Abstract. In this letter, the Brussels's urban heat island (UHI) effect on the near-surface air temperature time series of Uccle (the national suburban recording station of the Royal Meteorological Institute of Belgium) was estimated between 1955 and 2006 during the summer months. The UHI of Brussels was estimated using both ground-based weather stations and remote sensing imagery combined with a land surface scheme that includes a state-of-the-art urban parameterization, the Town Energy Balance scheme. Analysis of urban warming based on the remote sensing method reveals that the urban bias on minimum air temperature is rising at a higher rate, 2.5 times (2.85 ground-based observed) more, than on maximum temperature, with a linear trend of 0.15 °C (0.19 °C ground-based observed) and 0.06 °C (0.06 °C ground-based observed) per decade respectively. The summer-mean urban bias on the mean air temperature is 0.8 °C (0.9 °C ground-based observed). The results based on remote sensing imagery are compatible with estimates of urban warming based on weather stations. Therefore, the technique presented in this work is a useful tool in estimating the urban heat island contamination in long time series, countering the drawbacks of an ground-observational approach.

Quantifying Spatiotemporal Variations of Soil Moisture Control on Surface Energy Balance and Near-Surface Air Temperature

2017 ◽  
Vol 30 (18) ◽  
pp. 7105-7124 ◽  
Author(s):  
Clemens Schwingshackl ◽  
Martin Hirschi ◽  
Sonia I. Seneviratne
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Full Range ◽  
Surface Air Temperature ◽  
Temporal Variations ◽  
Heat Fluxes ◽  
Moisture Regime ◽  
Transitional Regime ◽  
Evaporative Fraction ◽  
Near Surface

Abstract Soil moisture plays a crucial role for the energy partitioning at Earth’s surface. Changing fractions of latent and sensible heat fluxes caused by soil moisture variations can affect both near-surface air temperature and precipitation. In this study, a simple framework for the dependence of evaporative fraction (the ratio of latent heat flux over net radiation) on soil moisture is used to analyze spatial and temporal variations of land–atmosphere coupling and its effect on near-surface air temperature. Using three different data sources (two reanalysis datasets and one combination of different datasets), three key parameters for the relation between soil moisture and evaporative fraction are estimated: 1) the frequency of occurrence of different soil moisture regimes, 2) the sensitivity of evaporative fraction to soil moisture in the transitional soil moisture regime, and 3) the critical soil moisture value that separates soil moisture- and energy-limited evapotranspiration regimes. The results show that about 30%–60% (depending on the dataset) of the global land area is in the transitional regime during at least half of the year. Based on the identification of transitional regimes, the effect of changes in soil moisture on near-surface air temperature is analyzed. Typical soil moisture variations (standard deviation) can impact air temperature by up to 1.1–1.3 K, while changing soil moisture over its full range in the transitional regime can alter air temperature by up to 6–7 K. The results emphasize the role of soil moisture for atmosphere and climate and constitute a useful benchmark for the evaluation of the respective relationships in Earth system models.

scholarly journals Simultaneous assessment of the summer urban heat island in Moscow megacity based on in situ observations, thermal satellite images and mesoscale modeling

2019 ◽  
Vol 12 (4) ◽  
pp. 74-95 ◽  
Author(s):  
Mikhail I. Varentsov ◽  
Mikhail Y. Grishchenko ◽  
Hendrik Wouters
Keyword(s):  
Remote Sensing ◽  
Spatial Structure ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Atmospheric Model ◽  
Heat Island ◽  
Near Surface ◽  
Urban Heat

This study compares three popular approaches to quantify the urban heat island (UHI) effect in Moscow megacity in a summer season (June-August 2015). The first approach uses the measurements of the near-surface air temperature obtained from weather stations, the second is based on remote sensing from thermal imagery of MODIS satellites, and the third is based on the numerical simulations with the mesoscale atmospheric model COSMO-CLM coupled with the urban canopy scheme TERRA_URB. The first approach allows studying the canopy-layer UHI (CLUHI, or anomaly of a near- surface air temperature), while the second allows studying the surface UHI (SUHI, or anomaly of a land surface temperature), and both types of the UHI could be simulated by the atmospheric model. These approaches were compared in the daytime, evening and nighttime conditions. The results of the study highlight a substantial difference between the SUHI and CLUHI in terms of the diurnal variation and spatial structure. The strongest differences are found at the daytime, at which the SUHI reaches the maximal intensity (up to 10°С) whereas the CLUHI reaches the minimum intensity (1.5°С). However, there is a stronger consistency between CLUHU and SUHI at night, when their intensities converge to 5–6°С. In addition, the nighttime CLUHI and SUHI have similar monocentric spatial structure with a temperature maximum in the city center. The presented findings should be taken into account when interpreting and comparing the results of UHI studies, based on the different approaches. The mesoscale model reproduces the CLUHI-SUHI relationships and provides good agreement with in situ observations on the CLUHI spatiotemporal variations (with near-zero biases for daytime and nighttime CLUHI intensity and correlation coefficients more than 0.8 for CLUHI spatial patterns). However, the agreement of the simulated SUHI with the remote sensing data is lower than agreement of the simulated CLUHI with in situ measurements. Specifically, the model tends to overestimate the daytime SUHI intensity. These results indicate a need for further in-depth investigation of the model behavior and SUHI–CLUHI relationships in general.

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 Linkages between land initialization of the NASA-Unified WRF v7 and biogenic isoprene emission estimates during the SEAC<sup>4</sup>RS and DISCOVER-AQ airborne campaigns

2017 ◽  
Author(s):  
Min Huang ◽  
Gregory R. Carmichael ◽  
James H. Crawford ◽  
Armin Wisthaler ◽  
Xiwu Zhan ◽  
...  
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Initial Conditions ◽  
Wrf Model ◽  
Surface Air Temperature ◽  
Heat Fluxes ◽  
Temperature Fields ◽  
Surface Model ◽  
Near Surface ◽  
Isoprene Emission

Abstract. Land and atmospheric initial conditions of the Weather Research and Forecasting (WRF) model are often interpolated from a different model output. We perform case studies during NASA's SEAC4RS and DISCOVER-AQ Houston airborne campaigns, demonstrating that initializing the Noah land surface model directly using a coarser resolution dataset North American Regional Reanalysis (NARR) led to significant positive biases in the coupled NASA-Unified WRF (NUWRF, version 7)'s (near-) surface air temperature and planetary boundary layer height (PBLH) around the Missouri Ozarks and Houston, Texas, as well as poorly partitioned latent and sensible heat fluxes. Replacing the land initial conditions with the output from a long-term offline Land Information System (LIS) simulation can effectively reduce the positive biases in NUWRF's surface air temperature fields by ~ 2 °C. We also show that the LIS land initialization can modify the surface air temperature errors almost ten times as effectively as applying a different atmospheric initialization method. The LIS-NUWRF based isoprene emission calculations by the Model of Emissions of Gases and Aerosols from Nature (MEGAN, version 2.1) are at least 20 % lower than those computed using the NARR-initialized NUWRF run, and are closer to the aircraft observation-derived emissions. Higher resolution MEGAN calculations are prone to amplified errors on small scales, possibly resulted from some limitations of MEGAN's parameterization and its inputs' uncertainty. This study emphasizes the importance of proper land initialization to the coupled atmospheric weather modeling and the follow-on emission modeling, which we anticipate to be also critical to accurately representing other processes included in air quality modeling and chemical data assimilation. Having more confidence in the weather inputs is also beneficial for determining and quantifying the other sources of uncertainties (e.g., parameterization, other input data) of the models that they drive.

scholarly journals Response of water temperatures and stratification to changing climate in three lakes with different morphometry

2016 ◽  
Author(s):  
Madeline R. Magee ◽  
Chin H. Wu
Keyword(s):  
Heat Flux ◽  
Wind Speed ◽  
Air Temperature ◽  
Sensible Heat Flux ◽  
Heat Fluxes ◽  
Surface Heat Fluxes ◽  
Lake Mendota ◽  
Abrupt Changes ◽  
Bottom Waters ◽  
Schmidt Stability

Abstract. Water temperatures in three morphometrically different lakes are simulated using a one-dimensional hydrodynamic lake model over the century (1911–2014) to elucidate the effects of increasing air temperature and decreasing wind speed on lake thermal variables (water temperature, stratification dates, strength of stratification, and surface heat fluxes). During the study period, epilimnetic temperatures increased, hypolimnetic temperatures decreased, and the length of the stratified season increased for the study lakes due to earlier stratification onset and later fall overturn. Additionally, there was an abrupt change in epilimnion temperature after 1930 in both Lake Mendota and Lake Wingra, and three changes, after 1934, 1995, and 2008 for Fish Lake. There was a significant change in the slope of trend of stratification duration after 1940 in Lake Mendota and a significant change in trend after 1981 for Fish Lake. Schmidt stability showed a statistically significant increasing trend for both deep lakes, with the larger trend and greater variability in the larger surface area lake. Sensible heat flux in all three lakes increases over the simulation period while longwave heat flux decreases. The shallow study lake had a greater change in latent heat flux and net heat flux, illustrating the role of lake depth to surface heat fluxes. Sensible heat flux in all three lakes had similar timing of abrupt changes, but the magnitude of the change increased with increasing depth. Abrupt changes in latent heat flux appear to be independent of lake morphometry, indicating that the timing of change may be primarily driven by climate. Perturbing drivers showed that increasing air temperature and decreasing wind speed caused earlier stratification onset and later fall overturn. For hypolimnetic water temperature, however, increasing air temperature warmed bottom waters while decreasing wind speed cooled bottom waters, indicating that the change of hypolimnetic temperatures globally may be influenced by local changes in wind speed. Overall, lake depth impacts the presence of stratification and magnitude of Schmidt stability, while lake surface area drives differences in hypolimnion temperature, hypolimnetic heating, variability of Schmidt stability, and stratification onset and fall overturn dates.

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