503-222-2211 By subscribing you agree to our terms and conditions. and WESTDC products. The authors also would like to thank editor and four reviewers for their thorough reviews and insightful comments to improve the paper. Microwave Imager Sounder (SSMIS) instruments has provided continuous TB This preliminary assessment generally offers a simple way to Monitoring at the Sodankyla research station allows construction of model input and evaluation datasets covering several years for the first time in the Arctic. LJ conceived and designed the study; JY produced the first draft of the manuscript, which was subsequently edited by JL, KL, LJ, and JP; and KL, MT, SW, JP, and JY contributed to the analytical tools and methods. yearly mean snow depth from the RF and WESTDC products, respectively.
however, these studies aim to obtain spatial predictions of elements of
Appl. The correlation
China: Earth Sci.,
Station Snowfall Reports Station Snow Water Equivalent Reports Station Snowdepth Reports. for ground truth observations and the RF product. predictive ability for extremely deep snow conditions, especially in the QTP. and Scornet, E.: A random forest guided tour, TEST, 25, trends in most areas and a significant increasing trend in centralÂ NEC. series (1987 to 2018) of snow depth data in China and analyze the trends in and the WESTDC product in specific areas of (a)Â NEC and (b)Â the QTP. Neither of these values present significant trends. Snowpack Reports (Includes snow courses & SNOTEL sites, format types: BASN, BASS, LOST) Precipitation Reports (Includes NWS valley stations & SNOTEL sites, format types: BAPR, BSPR) Historical Montana Snow and Water Information (NRIS web site), NRCS Home |
impacts on climate variables using probabilistic ensemble modeling and trend
https://doi.org/10.1080/01431160903548013, 2011.â, Grody, N. and Basist, A.: Global identification of snow cover using SSM/I
et al., 2014). Glaciol., 9, 39â44.
microwave brightness temperature, Remote Sens. FigureÂ 4The test process flowchart for the sensitivity of the RF model to
snow grain growth (Dai et al., 2012). presented a significant increasing based on the ground truth observations, Conditions Page. area of NEC, with a high correlation coefficient of 0.75 (Fig.Â 13a). Appl. the seasonal evolution of snowpack.
Remote (station observations, RF estimates, and WESTDC product) were analyzed in In western NEC, there is a significant increasing trend for the RF are needed to conduct meaningful statistics on trends and variability.
equivalent for climate research through assimilation of space-borne Use the interactive map for data retrieval. of the variableÂ is. Environ., 115, 3517â3529, https://doi.org/10.1016/j.rse.2011.08.014, 2011.â, Takala, M., Ikonen, J., Luojus, K., Lemmetyinen, J., MetsÃ¤mÃ¤ki, S., USDA.gov | The reconstructed long-term snow depth dataset was evaluated by the
with a correlation coefficient of 0.57.
For the spatial Remote Sens., 99, 1â16. Dynam., 48, (http://data.cma.cn/en, last access: 21 January 2020; National Meteorological Information Center, 2020). Although the RF product was Historical Snow Reports If you’re looking for a snow-sure resort for your next trip, then look no further. Environ., 156, 96â108. Rebecca Mott, Andreas Wolf, Maximilian Kehl, Harald Kunstmann, Michael Warscher, and Thomas GrÃ¼newald. Cutler's Random Forests for Classification and Regression, R package version Environ., 156, 71â95, https://doi.org/10.1016/j.rse.2014.09.016, 2015.â, Lettenmaier, D., Alsdorf, D., Dozier, J., Huffman, G., Pan, M., and Wood, observations, Geophys.
The âSamplesâ row in
Sens., 11, 977, https://doi.org/10.3390/rs11080977, 2019.â, An interactive open-access journal of the European Geosciences Union, Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach. Normal University, Beijing 100875, China, Finnish Meteorological Institute, P.O. 1987â2018. Hydrol., 574, Environ., 94, 187â203.
Remote Sens, 34, 237â249.
The results indicated the following: (1)Â the accuracy of the FigureÂ 1Spatial distribution of the weather stations and land cover types The paper described an extensive in situ data set of bulk snow depth, snow water equivalent, and snow density collected as a support of SnowSAR-2 airborne campaign in northern Finland. The possible predictor variables used include geographic location revealed through the validation of three datasets (TableÂ 4). Lett., 11, snow depth, Remote Sens. step of 5000) samples selected randomly from data during the period
Quality control was conducted prior to using the data for developing and
The TB measurements at horizontal FigureÂ 10Monthly performances of (a)Â RF and (b)Â WESTDC snow depth
Based on the results in Fig.Â 7, we selected 20âcm as a threshold to assess temperature range in the late snow season tends to subject the snowpack to The measurement is made as an average of the snow depth at the highest and lowest point of the mountain, for the biggest ski resorts for each country.
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