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2022 ◽  
Author(s):  
Dariusz Sebastian Ignatiuk ◽  
Małgorzata Błaszczyk ◽  
Tomasz Budzik ◽  
Mariusz Grabiec ◽  
Jacek Adam Jania ◽  
...  

Abstract. The warming of the Arctic climate is well documented, but the mechanisms of Arctic amplification are still not fully understood. Thus, monitoring of glaciological and meteorological variables and the environmental response to accelerated climate warming must be continued and developed in Svalbard. Long-term meteorological observations carried out in situ on glaciers in conjunction with glaciological monitoring are rare in the Arctic and significantly expand our knowledge about processes in the polar environment. This study presents the unique glaciological and meteorological data collected in 2009–2020 in southern Spitsbergen (Werenskioldbreen). The meteorological data are comprised of air temperature, relative humidity, wind speed and direction, shortwave and longwave upwelling and downwelling radiation on 10 minutes, hourly and daily timescale (2009–2020). The snow dataset includes 49 sampling points from 2009–2019 with the snow depth, snow bulk density and SWE data. The glaciological data consist of point and surface annual winter, summer and net balance for 2009–2020. The paper also includes modelling of the daily glacier surface ablation (2009–2020) based on the presented data. The high-quality and long-term datasets are expected to serve as accurate forcing data in hydrological and glaciological models and validation of remote sensing products. The datasets are available from the and Polish Polar Database (https://ppdb.us.edu.pl/) and Zenodo (https://doi.org/10.5281/zenodo.5791748, Ignatiuk, 2021a; https://doi.org/10.5281/zenodo.5792168, Ignatiuk, 2021b).


MAUSAM ◽  
2021 ◽  
Vol 42 (2) ◽  
pp. 201-204
Author(s):  
P. N. SEN

A mathematical, model for Quantitative Precipitation Forecasting (QPF) has been developed on the basis of physical and dynamical laws. The surface and upper air meteorological observations have been used as inputs in the model. The output is the rate of precipitation from which the amount of precipitation can be computed time integration. The model can be used operationally for rainfall forecasting.


2021 ◽  
Vol 14 (1) ◽  
pp. 57
Author(s):  
Siyuan Chen ◽  
Lichun Sui ◽  
Liangyun Liu ◽  
Xinjie Liu

Accurate estimation of gross primary productivity (GPP) is necessary to better understand the interaction of global terrestrial ecosystems with climate change and human activities. Light use efficiency (LUE)-based GPP models are widely used for retrieving several GPP products with various temporal and spatial resolutions. However, most LUE-based models assume a clear-sky condition, and the influence of diffuse radiation on GPP estimations has not been well considered. In this paper, a diffuse and direct (DDA) absorbed photosynthetically active radiation (APAR)-based method is proposed for better estimation of half-hourly GPP, which partitions APAR under diffuse and direct radiation conditions. Firstly, energy balance residual (EBR) FAPAR, moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) (MCD15A2H) and clumping index (CI) products, as well as solar radiation records supplied by FLUXNET2015 were used to calculate diffuse and direct APAR at a half-hourly scale. Then, an eddy covariance-LUE (EC-LUE) model and meteorological observations from FLUXNET2015 data sets were used for obtaining corresponding LUE values. A co-variation relationship between LUE and diffuse fraction was observed, and the LUE was higher under more diffuse radiation conditions. Finally, the DDA-based method was tested using the half-hourly FLUXNET GPP and compared with half-hourly GPP calculated using total APAR (GPP_TA). The results indicated that the half-hourly GPP estimated using the DDA-based method (GPP_DDA) was more accurate, giving higher R2 values, lower RMSE and RMSE* values (R2 varied from 0.565 to 0.682, RMSE ranged from 3.219 to 12.405 and RMSE* were within the range of 2.785 to 8.395) than the GPP_TA (R2 varied from 0.558 to 0.653, RMSE ranged from 3.407 to 13.081 and RMSE* were within the range of 3.321 to 9.625) across FLUXNET sites within different vegetation types. This study explored the effects of partitioning the diffuse and direct APAR on half-hourly GPP estimations, which demonstrates a higher agreement with FLUXNET GPP than total APAR-based GPP.


2021 ◽  
pp. 1-13
Author(s):  
Alex Priestley ◽  
Bernd Kulessa ◽  
Richard Essery ◽  
Yves Lejeune ◽  
Erwan Le Gac ◽  
...  

Abstract To understand snow structure and snowmelt timing, information about flows of liquid water within the snowpack is essential. Models can make predictions using explicit representations of physical processes, or through parameterization, but it is difficult to verify simulations. In situ observations generally measure bulk quantities. Where internal snowpack measurements are made, they tend to be destructive and unsuitable for continuous monitoring. Here, we present a novel method for in situ monitoring of water flow in seasonal snow using the electrical self-potential (SP) geophysical method. A prototype geophysical array was installed at Col de Porte (France) in October 2018. Snow hydrological and meteorological observations were also collected. Results for two periods of hydrological interest during winter 2018–19 (a marked period of diurnal melting and refreezing, and a rain-on-snow event) show that the electrical SP method is sensitive to internal water flow. Water flow was detected by SP signals before it was measured in conventional snowmelt lysimeters at the base of the snowpack. This initial feasibility study shows the utility of the SP method as a non-destructive snow sensor. Future development should include combining SP measurements with a high-resolution snow physics model to improve prediction of melt timing.


2021 ◽  
Author(s):  
Fabiana Castino ◽  
Bodo Wichura ◽  
Harald Schellander ◽  
Michael Winkler

<p>The characterization of the snow cover by snow water equivalent (SWE) is fundamental in several environmental applications, e.g., monitoring mountain water resources or defining structural design standards. However, SWE observations are usually rare compared to other snow measurements as snow depth (HS). Therefore, model-based methods have been proposed in past studies for estimating SWE, in particular for short timescales (e.g., daily). In this study, we compare two different approaches for SWE-data modelling. The first approach, based on empirical regression models (ERMs), provides the regional parametrization of the bulk snow density, which can be used to estimate SWE values from HS. In particular, we investigate the performances of four different schemes based on previously developed ERMs of bulk snow density depending on HS, date, elevation, and location. Secondly, we apply the semi-empirical multi-layer Δsnow model, which estimates SWE solely based on snow depth observations. The open source Δsnow model has been recently used for deriving a snow load map for Austria, resulting in an improved Austrian standard. A large dataset of HS and SWE observations collected by the National Weather Service in Germany (DWD) is used for calibrating and validating the models. This dataset consists of daily HS and three-times-a-week SWE observations from in total ~1000 stations operated by DWD over the period from 1950 to 2020. A leave-one-out cross validation is applied to evaluate the performance of the different model approaches. It is based on 185 time series of HS and SWE observations that are representative of the diversity of the regional snow climatology of Germany. Cross validation reveals for all ERMs: 90% of the modelled SWE time series have a root mean square error (RMSE) and a bias lower than 45 kg/m² and 2 kg/m², respectively. The Δsnow model shows the best performance with 90% of the modelled SWE time series having an RMSE lower than 30 kg/m² and bias similar to the ERMs. This comparative study provides new insights on the reliability of model-based methods for estimating SWE values. The results show that the Δsnow model and, to a lower degree, the developed ERMs can provide satisfactory performances even on short timescales. This suggest that these models can be used as reliable alternative to more complex thermodynamic snow models, even more if long-term meteorological observations aside HS are scarce.</p>


MAUSAM ◽  
2021 ◽  
Vol 50 (1) ◽  
pp. 71-76
Author(s):  
P. SANJEEVA RAO ◽  
L. S. RATHORE ◽  
T. J. GILLESPIE ◽  
H. S. KUSHWAHA

Hourly meteorological observations over a potato crop field were used to validate a biophysical model and different thresholds of relative humidity (RH) to simulate the onset. cessation and total wetness duration (WD). The model showed the capability to simulate multiple wet and dry conditions as well as prolonged moist conditions with a mean absolute error of less than an hour. The deviation between measured and estimated onset and total WD was more pronounced when only RH was used. However, under the prevailing agroclimatic conditions of potato growing regions in India, 80% RH threshold may adequately be accurate to estimate WD for many weather-based disease management advisories.


Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8498
Author(s):  
Tingting Zhu ◽  
Yiren Guo ◽  
Zhenye Li ◽  
Cong Wang

Photovoltaic power generation is highly valued and has developed rapidly throughout the world. However, the fluctuation of solar irradiance affects the stability of the photovoltaic power system and endangers the safety of the power grid. Therefore, ultra-short-term solar irradiance predictions are widely used to provide decision support for power dispatching systems. Although a great deal of research has been done, there is still room for improvement regarding the prediction accuracy of solar irradiance including global horizontal irradiance, direct normal irradiance and diffuse irradiance. This study took the direct normal irradiance (DNI) as prediction target and proposed a Siamese convolutional neural network-long short-term memory (SCNN-LSTM) model to predict the inter-hour DNI by combining the time-dependent spatial features of total sky images and historical meteorological observations. First, the features of total sky images were automatically extracted using a Siamese CNN to describe the cloud information. Next, the image features and meteorological observations were fused and then predicted the DNI in 10-min ahead using an LSTM. To verify the validity of the proposed SCNN-LSTM model, several experiments were carried out using two-year historical observation data provided by the National Renewable Energy Laboratory (NREL). The results show that the proposed method achieved nRMSE of 23.47% and forecast skill of 24.51% for the whole year of 2014, and it also did better than some published methods especially under clear sky and rainy days.


Author(s):  
Slobodianyk K. L. ◽  
Semerhei-Chumachenko A. B. ◽  
Veretnova V. O.

The paper presents the results of a study of heavy precipitation in the form of rain (> 30 mm/12 h) using data from the meteorological observations and atmospheric reanalysis ERA5 at the Kherson weather station in 2005-2021.Detected that at the Kherson there were only 19 cases of heavy rainfall, which occurred only in the warm half of the year with a maximum recurrence in July. Compared to 1961-1990, the number of heavy rains of 2005-2021 increased in July and June, and decreased in August.Determined that most of the real cases of increased precipitation in Kherson are in good agreement with the results of the ERA5 reanalysis, but in almost a third of the simulation episodes did not show heavy precipitation at the Kherson coordinates or their center was shifted.Heavy rains in Kherson were formed in a field of low atmospheric pressure, with a weak northwest wind and accompanied by thunderstorms.Clarified that most episodes of heavy rainfall in Kherson in 2005-2021 are associated with the movement of southern cyclones, others formed on the southern periphery of the anticyclone in the southwestern direction of the jet stream in the troposphere.


2021 ◽  
Vol 13 (23) ◽  
pp. 4915
Author(s):  
Zhenmin Niu ◽  
Nai’ang Wang ◽  
Nan Meng ◽  
Jiang Liu ◽  
Xueran Liang ◽  
...  

Mega-dunes in the lake group area of the Badain Jaran Sand Sea, China, are generally taller than dunes in the non-lake group area. This spatial distribution of dune heights may provide a new perspective on the controversy regarding the dunes’ formation mechanism. In this study, we calculated the relative heights and slopes of individual dunes based on a digital elevation model, and we confirmed the height distribution of abnormally tall dunes in the lake group area of the sand sea. It was also found that slopes of more than 10° in the lake group area are more common than those in the non-lake group area. Based on meteorological observations, coupled with the measurement of water content in the sand layers, we propose a conceptual model demonstrating that moisture exchange between the lakes and soil via non-rainfall water will humidify dune slopes and form a more favorable accumulation environment for aeolian sand, thus increasing dune heights. Although long-term observations are yet to be carried out, the present study can be used as evidence for understanding the basis of dune formation in the lake group area and assessing groundwater utilization in deserts.


2021 ◽  
Author(s):  
Evgenii Churiulin ◽  
Vladimir Kopeikin ◽  
Markus Übel ◽  
Jürgen Helmert ◽  
Jean-Maria Bettems ◽  
...  

Abstract. Climatic changes towards warmer temperatures require the need to improve the simplified vegetation scheme of the regional climate model COSMO-CLM, which is not capable of modelling complex processes which depend on temperature, water availability, and day length. Thus, we have implemented the physically based Ball-Berry approach coupled with photosynthesis processes based on Farquhar and Collatz models for C3 and C4 plants in the regional climate model COSMO-CLM (CCLM v 5.16). The implementation of the new algorithms includes the replacement of the “one-big leaf” approach by a “two-big leaf” one. We performed single column simulations with COSMO-CLM over three observational sites with C3 grass plants in Germany for the period from 2010 to 2015 (Parc, Linden and Lindenberg domain). Hereby, we tested three alternative formulations of the new algorithms against a reference simulation (CCLMref) with no changes. The first formulation (CCLM3.5) adapts the algorithms for stomatal resistance from the Community Land Model (CLM v3.5), which depend on leaf photosynthesis, CO2 partial and vapor pressure and maximum stomatal resistance. The second one (CCLM4.5) includes a soil water stress function as in CLM v4.5. The third one (CCLM4.5e) is similar to CCLM4.5, but with adapted equations for dry leaf calculations. The results revealed major differences in the annual cycle of stomatal resistance compared to the original algorithm (CCLMref) of the reference simulation. The largest changes in stomatal resistance are observed from October to April when stomata are closed while summer values are generally less than control values that come closer to measured values. The results indicate that changes in stomatal resistance and photosynthesis algorithms can improve the accuracy of other parameters of the COSMO-CLM model (e.g.: transpiration rate or total evapotranspiration). These results were received by comparing COSMO-CLM parameters with FLUXNET data, meteorological observations at the sites, and GLEAM and HYRAS datasets.


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