Impact of Land Surface Processes on a Record‐Breaking Rainfall Event on May 06–07, 2017, in Guangzhou, China

Gao, Zhibo; Zhu, Jun; Guo, Yan; Luo, Neng; Fang, Yuan; Wang, Tiantian

doi:10.1029/2020jd032997

Cited by 33 publications

(29 citation statements)

References 103 publications

(96 reference statements)

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“…The impact of uncertainties in the initial condition during prediction might be dominant, especially at short ranges. Therefore, we chose 11 members (gec00-gep10) of the NCEP Global Ensemble Forecast System (GEFS) [42] as the initial condition and boundary condition of WRF to discuss the atmospheric mitigating effects with different WRF-related methods in DInSAR.…”

Section: Conflicts Of Interestmentioning

confidence: 99%

Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model

Dou

Chai

2021

Remote Sensing

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The interferometric synthetic aperture radar (InSAR) technique is widely utilized to measure ground-surface displacement. One of the main limitations of the measurements is the atmospheric phase delay effects. For satellites with shorter wavelengths, the atmospheric delay mainly consists of the tropospheric delay influenced by temperature, pressure, and water vapor. Tropospheric delay can be calculated using numerical weather prediction (NWP) model at the same moment as synthetic aperture radar (SAR) acquisition. Scientific researchers mainly use ensemble forecasting to produce better forecasts and analyze the uncertainties caused by physic parameterizations. In this study, we simulated the relevant meteorological parameters using the ensemble scheme of the stochastic physic perturbation tendency (SPPT) based on the weather research forecasting (WRF) model, which is one of the most broadly used NWP models. We selected an area in Foshan, Guangdong Province, in the southeast of China, and calculated the corresponding atmospheric delay. InSAR images were computed through data from the Sentinel-1A satellite and mitigated by the ensemble mean of the WRF-SPPT results. The WRF-SPPT method improves the mitigating effect more than WRF simulation without ensemble forecasting. The atmospherically corrected InSAR phases were used in the stacking process to estimate the linear deformation rate in the experimental area. The root mean square errors (RMSE) of the deformation rate without correction, with WRF-only correction, and with WRF-SPPT correction were calculated, indicating that ensemble forecasting can significantly reduce the atmospheric delay in stacking. In addition, the ensemble forecasting based on a combination of initial uncertainties and stochastic physic perturbation tendencies showed better correction performance compared with the ensemble forecasting generated by a set of perturbed initial conditions without considering the model’s uncertainties.

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Section: Conflicts Of Interestmentioning

confidence: 99%

Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model

Dou

Chai

2021

Remote Sensing

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“…Studies found that some hot weather is sensitive to near-surface conditions and LSCs [4,8,9]. Therefore, the impact of LSC is becoming increasingly important for understanding hot weather in EC [10].…”

Section: Introductionmentioning

confidence: 99%

“…The land surface has long been recognized as second in importance only to oceansurface temperature as a driver of climate [11,12]. LSCs, which are a crucial part of the land-surface model and the basic forcing parameters of land-surface boundary conditions [13], can affect the distribution of land-surface energy fluxes and local or regional mesoscale circulation [10,14]. A better description of land-surface characteristic parameters (LSCPs) can improve regional land simulation, and even improve land-atmosphere interactions [13,[15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…In the WRF model, whether traditionally looking up tables dependent on vegetation type or using climatology-satellite-based surface characteristics, it is difficult to match fine weather forecasts or high-resolution simulations. Near-real-time satellite-based LSCPs and assimilation-based SM data are used in regional numerical models, which greatly improve model performance [8,10,13,37,38]. At Earth's surface, vegetation-related parameters including LAI, land surface albedo, and SM control the partitioning of energy fluxes and water-vapor transport; thus, land-surface conditions in some regions yield more significant feedback influence [39].…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Pan

et al. 2021

Remote Sensing

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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.

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“…According to previous studies, climate models have reasonable con dence in the simulation of global surface air temperature (SAT), climate extremes, atmospheric circulation and other elements (Zhou et al, 2006;Lorenz R et al, 2014;Sillmann J et al 2013aSillmann J et al , 2013bZhou et al 2014;Chen and Sun, 2015;Gao et al, 2021), however, there is a large uncertainty in the models' simulations of DTR. The IPCC Fifth Assessment Report (2013) indicated that the CMIP5 model can well reproduce the large-scale surface temperature patterns, but that in many regions there are large uncertainties in the trends and possible interpretations of DTR (Christy J R, et al, 2006;Fall et al, 2011;Zhou and Ren, 2011).…”

Section: Introductionmentioning

confidence: 99%

DTR in Winter Wheat Growing Regions of China: CMIP6 Models Evaluation and Comparation

Xie

Wang

Yan

2021

Preprint

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Winter wheat is widely planted in China. The changes of winter wheat yield and quality are related to the food security of human society. Climate change has an important impact on the yield and quality of winter wheat. Diurnal temperature range (DTR) is an important factor affecting the yield and protein content of winter wheat. Furthermore, climate model is one of the main sources of error in crop model simulations of yields. Therefore, how to improve the accuracy of climate data has become an important concern for scholars.Previous model evaluations for the entire country or region cannot answer which model is suitable for the estimation of future winter wheat yield. Therefore, we evaluated the ability of climate models to simulate DTR within the range of winter wheat growing regions in China to identify the most suitable climate models for winter wheat yield and quality projections. The results show that CMIP6 models can basically reproduce the DTR of winter wheat-growing regions in China, but there are discrepancies in the simulations between nationwide and winter wheat-growing regions. EC-Earth3-Veg has the best simulation of climate DTR for wheat-growing regions (TS=0.848) and nationwide (TS=0.842), and ACCESS-CM2 has the strongest ability to simulate the annual growing season DTR (TS=0.46). In summary, in the estimation of future winter wheat yield, attention should be given to the selection of models suitable for the actual growing regions and the growing seasons of winter wheat.

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Impact of Land Surface Processes on a Record‐Breaking Rainfall Event on May 06–07, 2017, in Guangzhou, China

Cited by 33 publications

References 103 publications

Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model

Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model

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

DTR in Winter Wheat Growing Regions of China: CMIP6 Models Evaluation and Comparation

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