Mapping Regional Turbulent Heat Fluxes via Assimilation of MODIS Land Surface Temperature Data into an Ensemble Kalman Smoother Framework

Estimation of turbulent heat fluxes via variational data assimilation (VDA) approaches has been the subject of several studies. The VDA approaches need an adjoint model that is difficult to derive. In this study, remotely sensed land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are assimilated into the heat diffusion equation within an ensemble Kalman smoother (EnKS) approach to estimate turbulent heat fluxes. The EnKS approach is tested in the Heihe River Basin (HRB) in northwest China. The results show that the EnKS approach can estimate turbulent heat fluxes by assimilating low temporal resolution LST data from MODIS. The findings indicate that the EnKS approach performs fairly well in various hydrological and vegetative conditions. The estimated sensible (H) and latent (LE) heat fluxes are compared with the corresponding observations from large aperture scintillometer systems at three sites (namely, Arou, Daman, and Sidaoqiao) in the HRB. The turbulent heat flux estimates from EnKS agree reasonably well with the observations, and are comparable to those of the VDA approach. The EnKS approach also provides statistical information on the H and LE estimates. It is found that the uncertainties of H and LE estimates are higher over wet and/or densely vegetated areas (grassland and forest) compared to the dry and/or slightly vegetated areas (cropland, shrubland, and barren land)

Evaluation of the Weak Constraint Data Assimilation Approach for Estimating Turbulent Heat Fluxes at Six Sites

A number of studies have estimated turbulent heat fluxes by assimilating sequences of land surface temperature (LST) observations into the strong constraint-variational data assimilation (SC-VDA) approaches. The SC-VDA approaches do not account for the structural model errors and uncertainties in the micrometeorological variables. In contrast to the SC-VDA approaches, the WC-VDA approach (the so-called weak constraint-VDA) accounts for the effects of structural and model errors by adding a model error term. In this study, the WC-VDA approach is tested at six study sites with different climatic and vegetative conditions. Its performance is also compared with that of SC-VDA at the six study sites. The results show that the WC-VDA produces 10.16% and 10.15% lower root mean square errors (RMSEs) for sensible and latent heat flux estimates compared with the SC-VDA approach. The model error term can capture errors in the turbulent heat flux estimates due to errors in LST and micrometeorological measurements, as well as structural model errors, and does not allow those errors to adversely affect the turbulent heat flux estimates. The findings also indicate that the estimated model error term varies reasonably well, so as to capture the misfit between predicted and observed net radiation in different hydrological and vegetative conditions. Finally, synthetically generated positive (negative) noises are added to the hydrological input variables (e.g., LST, air temperature, air humidity, incoming solar radiation, and wind speed) to examine whether the WC-VDA approach can capture those errors. It was found that the WC-VDA approach accounts for the errors in the input data and reduces their effect on the turbulent heat flux estimates

Land use change effects on extreme flood in the Kelantan basin using hydrological model

Land use and land cover (LULC) change results in increased of flood frequency and severity. The increase of annual runoff which is caused by urban development, heavy deforestation, or other anthropogenic activities occurs within the catchment areas. Therefore, accurate and continuous LULC change information is vital in quantifying flood hydrograph for any given time. Many studies showed the effect of land use change on flood based on hydrological response (i.e., peak discharge and runoff volume). In this study, a distributed hydrological modeling and GIS approach were applied for the assessment of land use impact in the Kelantan Basin. The assessment focuses on the runoff contributions from different land use classes and the potential impact of land use changes on runoff generation. The results showed that the direct runoff from developmental area, agricultural area, and grassland region is dominant for a flood event compared with runoff from other land-covered areas in the study area. The urban areas or lower planting density areas tend to increase for runoff and for the monsoon season floods, whereas the inter-flow from forested and secondary jungle areas contributes to the normal flow

Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations

Accurate estimation of the satellite-based global terrestrial latent heat flux (LE) at high spatial and temporal scales remains a major challenge. In this study, we introduce a Bayesian model averaging (BMA) method to improve satellite-based global terrestrial LE estimation by merging five process-based algorithms. These are the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product algorithm, the revised remote-sensing-based Penman-Monteith LE algorithm, the Priestley-Taylor-based LE algorithm, the modified satellite-based Priestley-Taylor LE algorithm, and the semi-empirical Penman LE algorithm. We validated the BMA method using data for 2000–2009 and by comparison with a simple model averaging (SA) method and five process-based algorithms. Validation data were collected for 240 globally distributed eddy covariance tower sites provided by FLUXNET projects. The validation results demonstrate that the five process-based algorithms used have variable uncertainty and the BMA method enhances the daily LE estimates, with smaller root mean square errors (RMSEs) than the SA method and the individual algorithms driven by tower-specific meteorology and Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorological data provided by the NASA Global Modeling and Assimilation Office (GMAO), respectively. The average RMSE for the BMA method driven by daily tower-specific meteorology decreased by more than 5 W/m2 for crop and grass sites, and by more than 6 W/m2 for forest, shrub, and savanna sites. The average coefficients of determination (R2) increased by approximately 0.05 for most sites. To test the BMA method for regional mapping, we applied it for MODIS data and GMAO-MERRA meteorology to map annual global terrestrial LE averaged over 2001–2004 for spatial resolution of 0.05°. The BMA method provides a basis for generating a long-term global terrestrial LE product for characterizing global energy, hydrological, and carbon cycles

Thank You to Our 2022 Peer Reviewers

On behalf of the journal, AGU, and the scientific community, the editors of Geophysical Research Letters would like to sincerely thank those who reviewed manuscripts for us in 2022. The hours reading and commenting on manuscripts not only improve the manuscripts, but also increase the scientific rigor of future research in the field. With the advent of AGU\u27s data policy, many reviewers have also helped immensely to evaluate the accessibility and availability of data, and many have provided insightful comments that helped to improve the data presentation and quality. We greatly appreciate the assistance of the reviewers in advancing open science, which is a key objective of AGU\u27s data policy. We particularly appreciate the timely reviews in light of the demands imposed by the rapid review process at Geophysical Research Letters. We received 6,687 submissions in 2022 and 5,247 reviewers contributed to their evaluation by providing 8,720 reviews in total. We deeply appreciate their contributions in these challenging times

Thank You to Our 2019 Peer Reviewers

On behalf of the journal, AGU, and the scientific community, the editors would like to sincerely thank those who reviewed the manuscripts for Geophysical Research Letters in 2019. The hours reading and commenting on manuscripts not only improve the manuscripts but also increase the scientific rigor of future research in the field. We particularly appreciate the timely reviews in light of the demands imposed by the rapid review process at Geophysical Research Letters. With the revival of the “major revisions” decisions, we appreciate the reviewers’ efforts on multiple versions of some manuscripts. With the advent of AGU’s data policy, many reviewers have helped immensely to evaluate the accessibility and availability of data associated with the papers they have reviewed, and many have provided insightful comments that helped to improve the data presentation and quality. We greatly appreciate the assistance of the reviewers in advancing open science, which is a key objective of AGU’s data policy. Many of those listed below went beyond and reviewed three or more manuscripts for our journal, and those are indicated in italics.Key PointThe editors thank the 2019 peer reviewersPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162718/2/grl60415.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162718/1/grl60415_am.pd

Monthly surface solar radiation data over China (2000-2017) by merging satellite cloud and aerosol data with ground-based sunshine duration data

Surface incident solar radiation (Rs) is a key component of the surface radiation budget. It drives the global climate system and impacts the global energy balance and the hydrological and carbon cycles. Great progress has been made in the detection of variations in surface solar radiation (Rs) from meteorological observations, satellite retrieval and reanalysis. However, each type of estimation has its advantages and disadvantages. It has been shown that sunshine duration (SunDu)-derived Rs data can provide reliable long-term Rs variation over China; however, these data are spatially discontinuous. Therefore, we merged SunDu-derived Rs data with satellite-derived cloud fraction (MODAL2 M CLD) and CERES SYN aerosol optical depth (AOD) data to generate Rs data by the geographically weighted regression method. This dataset provides the monthly Rs from 2000 to 2017 over China with the spatial resolution of 0.1°

Quantifying and adjusting the impact of urbanization on the observed surface wind speed over China from 1985 to 2017

The observed surface wind speed (SWS) has declined across China over the last four decades, but the mechanisms responsible for this decline have been explored without reaching a consensus. In this study, we develop a physical method to quantify and adjust for the impact of urbanization around weather stations on the observed SWS over China from 1985 to 2017. The urbanization impact factor on the SWS is calculated based on Monin–Obukhov similarity theory, and the aerodynamic roughness length and zero-plane displacement height at each weather station are calculated yearly based on a 30-meter resolution satellite land cover product. The results show that urbanization around weather stations reduces the observed SWS by 11% on average over China. The urbanization impact on the observed SWS is the highest in southeastern China at 19% and the lowest over the Tibetan Plateau at 4%. Urbanization decreases the observed SWS by 9% over northwestern China and by 12% over northeastern China, northern China and southern China. More importantly, the proposed method can easily adjust for the urbanization impact on the observed SWS. After adjustment, the SWS appears to have started recovering during the 1990s, and the decreasing trend of SWS during the study period is nearly zero. The results shown here indicate that the observed decreasing trend of SWS from 1985 to 2017 over China is an observational local bias and does not reflect large-scale climatic variation. This inference is also consistent with geostrophic wind theory predictions; i.e., SWS exhibits strong decadal variability, but its long-term trend is negligible

Mapping the representativeness of precipitation measurements in Mainland China

Meteorological observations provide essential data for weather forecasting and climate change studies. Whether the measured data can accurately support such applications closely relates to the representativeness of the data collected, which depends on both the scale of observation and the density of the measurement network. Precipitation presents in the form of events and is discontinuous both in time and space. Gauge observations of precipitation could provide fundamental data but have difficulty quantitatively assessing precipitation system scale. Therefore, assessments on the representativeness of precipitation at synoptic and climatological scales remain needed. Here, we show the first high-resolution map of the representativeness of precipitation over Mainland China based on the latest satellite data. Our results show that the daily precipitation spatial consistency is the highest in eastern China and lowest on the Tibetan Plateau. However, the pattern of the monthly spatial consistency is different and is the highest over Northeast China Plain, the Loess Plateau, and the Middle–Lower Yangtze Plain. Compared to the density of rain gauges, we find that the current national station network with ∼2400 stations still has difficulty supporting synoptic studies in western China. However, for climate change studies based on monthly data, the density of the national reference climatological station network is sufficient, except in the western Tibetan Plateau and deserts with no available stations. For climatological studies, the quality of precipitation gauge observations is more important than its spatial density. Our results could provide great practical significance for considering the layout of rain gauges