Estimation of net shortwave radiation flux of western Himalayan snow cover during clear sky days using remote sensing and meteorological data

2014 ◽  
Vol 5 (1) ◽  
pp. 83-92 ◽  
Author(s):  
H.S. Gusain ◽  
V.D. Mishra ◽  
M.K. Arora
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Radiation Flux ◽  
Meteorological Data ◽  
Shortwave Radiation ◽  
Clear Sky

scholarly journals Parameter sensitivity of a distributed enhanced temperature-index melt model

2013 ◽  
Vol 54 (63) ◽  
pp. 311-321 ◽  
Author(s):  
Martin Heynen ◽  
Francesca Pellicciotti ◽  
Marco Carenzo
Keyword(s):  
Model Uncertainty ◽  
Meteorological Data ◽  
Lapse Rate ◽  
Shortwave Radiation ◽  
Temperature Threshold ◽  
Temperature Index ◽  
Temperature Lapse Rate ◽  
Clear Sky ◽  
The Andes ◽  
Optimal Value

AbstractWe investigate the sensitivity of a distributed enhanced temperature-index (ETI) melt model, in order to understand which parameters have the largest influence on model outputs and thus need to be accurately known. We use melt and meteorological data from two Alpine glaciers and one glacier in the Andes of Chile. Sensitivity analysis is conducted in a systematic way in terms of parameters and the different conditions (day, night, clear-sky, overcast), melt seasons and glaciers examined. The sensitivity of total melt to changes in individual parameters is calculated using a local method around the optimal value of the parameters. We verify that the parameters are optimal at the distributed scale and assess the model uncertainty induced by uncertainty in the parameters using a Monte Carlo technique. Model sensitivity to parameters is consistent across melt seasons, glaciers, different conditions and the daily statistics examined. The parameters to which the model is most sensitive are the shortwave-radiation factor, the temperature lapse rate for extrapolation of air temperature, the albedo parameters, the temperature threshold and the cloud transmittance factor parameters. A parameter uncertainty of 5% results in a model uncertainty of 5.6% of mean melt on Haut Glacier d’Arolla, Switzerland.

Seasonal to sub-seasonal hydrological forecast in Central Asia to improve water management and mitigate hydrometeorological risks

2021 ◽  
Author(s):  
Abror Gafurov ◽  
Olga Kalashnikova ◽  
Uktam Adkhamov ◽  
Akmal Gafurov ◽  
Adkham Mamaraimov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Resources ◽  
Central Asia ◽  
Snow Cover ◽  
Water Availability ◽  
Meteorological Data ◽  
River Basins ◽  
Linear Regression Method ◽  
Operational Mode ◽  
Hydrological Forecast

<p>Central Asia is facing a water shortage due to the negative impacts of climate change and demographic development. Water resources in this region originate mainly in the mountains of Pamir and Tian-Shan due to snow-and glacier melt. However, a limited observation network is available in these mountain systems and many are malfunctioning. Thus, the region needs new innovative methods to forecast seasonal and sub-seasonal water availability to ensure better water resources management and mitigate hydro-meteorological risks.</p><p>In this study, we present the results of our efforts for many years to develop a forecasting tool and implementation in the region. Since the region has limited observed meteorological data, we use primarily remote sensing data on snow cover for this purpose. We apply the MODIS snow cover data that is processed, including cloud removal, using the MODSNOW-Tool. We have applied this tool, which can be used to monitor snow cover in an operational mode and forecast water availability for the vegetation period but also for the monthly scale using the multiple linear regression method.</p><p>Our results show that snow is important in most of the river basins and can also be used as a single predictor to forecast seasonal water availability. Especially, in remote areas with limited observations, this approach gives a possibility of forecasting water availability for different time period. Besides seasonal hydrological forecast, the MODSNOW-Tool was also used to forecast water availability for upcoming months. The validity of forecasts were tested against observed discharge for the last 20 years and mostly above 70 % verification was achieved. Additionally to remote sensing based snow cover data, observed meteorological information was also used as predictors and improved the validity of forecast models in some river basins.</p><p>The implementation of the MODSNOW-Tool to improve the hydrological forecast was done for 28 river basins in Central Asia that are located in the territories of five post-Soviet countries Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan.  The MODSNOW-Tool was also implemented at the National Hydrometeorological Services (NHMS) of each post-Soviet country.</p>

scholarly journals Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

2019 ◽  
Vol 11 (12) ◽  
pp. 1456 ◽  
Author(s):  
Ya-Lun S. Tsai ◽  
Andreas Dietz ◽  
Natascha Oppelt ◽  
Claudia Kuenzer
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Meteorological Data ◽  
Weather Condition ◽  
Natural Phenomenon ◽  
Snow Cover Extent ◽  
Wet Snow ◽  
Snow Conditions ◽  
Interferometric Sar ◽  
Processing Techniques

The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments.

Evaluation of net shortwave radiation over China with a regional climate model

2020 ◽  
Vol 80 (2) ◽  
pp. 147-163
Author(s):  
X Liu ◽  
Y Kang ◽  
Q Liu ◽  
Z Guo ◽  
Y Chen ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Radiant Energy ◽  
Energy System ◽  
Radiation Budget ◽  
Shortwave Radiation ◽  
Monsoon Region ◽  
Clear Sky ◽  
Sky Conditions

The regional climate model RegCM version 4.6, developed by the European Centre for Medium-Range Weather Forecasts Reanalysis, was used to simulate the radiation budget over China. Clouds and the Earth’s Radiant Energy System (CERES) satellite data were utilized to evaluate the simulation results based on 4 radiative components: net shortwave (NSW) radiation at the surface of the earth and top of the atmosphere (TOA) under all-sky and clear-sky conditions. The performance of the model for low-value areas of NSW was superior to that for high-value areas. NSW at the surface and TOA under all-sky conditions was significantly underestimated; the spatial distribution of the bias was negative in the north and positive in the south, bounded by 25°N for the annual and seasonal averaged difference maps. Compared with the all-sky condition, the simulation effect under clear-sky conditions was significantly better, which indicates that the cloud fraction is the key factor affecting the accuracy of the simulation. In particular, the bias of the TOA NSW under the clear-sky condition was <±10 W m-2 in the eastern areas. The performance of the model was better over the eastern monsoon region in winter and autumn for surface NSW under clear-sky conditions, which may be related to different levels of air pollution during each season. Among the 3 areas, the regional average biases overall were largest (negative) over the Qinghai-Tibet alpine region and smallest over the eastern monsoon region.

scholarly journals Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers

Geosciences ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 130
Author(s):  
Sebastian Rößler ◽  
Marius S. Witt ◽  
Jaakko Ikonen ◽  
Ian A. Brown ◽  
Andreas J. Dietz
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Air Temperature ◽  
Kendall Test ◽  
The Arctic ◽  
Boreal Winter ◽  
Snowmelt Runoff ◽  
River Catchment ◽  
Catchment Areas ◽  
Runoff Model

The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to flooding in many places. Since the last severe spring floods occurred in the region in 2018, this raises the question of whether more frequent occurrences can be expected in the future. To assess the variability of snowmelt related flooding we used snow cover maps (derived from the DLR’s Global SnowPack MODIS snow product) and freely available data on runoff, precipitation, and air temperature in eight unregulated river catchment areas. A trend analysis (Mann-Kendall test) was carried out to assess the development of the parameters, and the interdependencies of the parameters were examined with a correlation analysis. Finally, a simple snowmelt runoff model was tested for its applicability to this region. We noticed an extraordinary variability in the duration of snow cover. If this extends well into spring, rapid air temperature increases leads to enhanced thawing. According to the last flood years 2005, 2010, 2018, and 2020, we were able to differentiate between four synoptic flood types based on their special hydrometeorological and snow situation and simulate them with the snowmelt runoff model (SRM).

scholarly journals A Simple Method for Converting 1-km Resolution Daily Clear-Sky LST into Real LST

2020 ◽  
Vol 12 (10) ◽  
pp. 1641
Author(s):  
Yunfei Zhang ◽  
Yunhao Chen ◽  
Jing Li ◽  
Xi Chen
Keyword(s):  
Land Surface ◽  
Missing Values ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Shortwave Radiation ◽  
Simple Method ◽  
The Real ◽  
Independent Variables ◽  
Clear Sky ◽  
Reconstruction Methods

Land-surface temperature (LST) plays a key role in the physical processes of surface energy and water balance from local through global scales. The widely used one kilometre resolution daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product has missing values due to the influence of clouds. Therefore, a large number of clear-sky LST reconstruction methods have been developed to obtain spatially continuous LST datasets. However, the clear-sky LST is a theoretical value that is often an overestimate of the real value. In fact, the real LST (also known as cloudy-sky LST) is more necessary and more widely used. The existing cloudy-sky LST algorithms are usually somewhat complicated, and the accuracy needs to be improved. It is necessary to convert the clear-sky LST obtained by the currently better-developed methods into cloudy-sky LST. We took the clear-sky LST, cloud-cover duration, downward shortwave radiation, albedo and normalized difference vegetation index (NDVI) as five independent variables and the real LST at the ground stations as the dependent variable to perform multiple linear regression. The mean absolute error (MAE) of the cloudy-sky LST retrieved by this method ranged from 3.5–3.9 K. We further analyzed different cases of the method, and the results suggested that this method has good flexibility. When we chose fewer independent variables, different clear-sky algorithms, or different regression tools, we also achieved good results. In addition, the method calculation process was relatively simple and can be applied to other research areas. This study preliminarily explored the influencing factors of the real LST and can provide a possible option for researchers who want to obtain cloudy-sky LST through clear-sky LST, that is, a convenient conversion method. This article lays the foundation for subsequent research in various fields that require real LST.

Sign in / Sign up

Export Citation Format

Share Document