The European Cooperation in Science and Technology (COST) Action ES1404 “HarmoSnow”, entitled, “A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction” (2014-2018) aims to coordinate efforts in Europe to harmonize approaches to validation, and methodologies of snow measurement practices, instrumentation, algorithms and data assimilation (DA) techniques. One of the key objectives of the action was “Advance the application of snow DA in numerical weather prediction (NWP) and hydrological models and show its benefit for weather and hydrological forecasting as well as other applications.” This paper reviews approaches used for assimilation of snow measurements such as remotely sensed and in situ observations into hydrological, land surface, meteorological and climate models based on a COST HarmoSnow survey exploring the common practices on the use of snow observation data in different modeling environments. The aim is to assess the current situation and understand the diversity of usage of snow observations in DA, forcing, monitoring, validation, or verification within NWP, hydrology, snow and climate models. Based on the responses from the community to the questionnaire and on literature review the status and requirements for the future evolution of conventional snow observations from national networks and satellite products, for data assimilation and model validation are derived and suggestions are formulated towards standardized and improved usage of snow observation data in snow DA. Results of the conducted survey showed that there is a fit between the snow macro-physical variables required for snow DA and those provided by the measurement networks, instruments, and techniques. Data availability and resources to integrate the data in the model environment are identified as the current barriers and limitations for the use of new or upcoming snow data sources. Broadening resources to integrate enhanced snow data would promote the future plans to make use of them in all model environments.
<div><span>Snow as a major part of the cryosphere is an important component of Earth&#8217;s hydrological cycle and energy balance. Understanding the microstructural, macrophysical, thermal and optical properties of the snowpack is essential for integration into numerical models and there is a great need for accurate snow data at different spatial and temporal resolutions to address the challenges of changing snow conditions.</span></div><div><span>Physical snow properties are currently determined by traditional ground-based measurements as well as remote sensing, over a range of temporal and spatial scales, following considerable developments in instrument technology over recent years.&#160;</span></div><div><span>Data assimilation (DA)</span><span> methods are widely used</span> <span>to combine data from different observations</span><span> with numerical model using uncertainties </span><span>of observed and modeled variables&#160; to produce an optimal estimate. DA provides a reliable improvement of the initial states of the numerical model and a benefit for hydrological and snow model forecasts. </span></div><div>&#160;</div><div><span>European efforts to harmonize approaches for validation, and methodologies of snow measurement practices, instrumentation, algorithms and data assimilation techniques were coordinated by the European Cooperation in Science and Technology (COST) Action ES1404 &#8220;HarmoSnow&#8221;, entitled, &#8220;A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction&#8221; (2014-2018) .</span></div><div><span>One of the key objectives of the action was &#8220;Advance the application of snow DA in numerical weather prediction (NWP) and hydrological models, and show its benefit for weather and hydrological forecasting as well as other applications.&#8221;&#160;</span></div><div><span>One key result from COST HarmoSnow is a better knowledge about the diversity of usage of snow observations in DA, forcing, monitoring, validation, or verification within NWP, hydrology, snow and climate models. The main parts of this knowledge are retrieved from a COST HarmoSnow survey exploring the common practices on the use of snow observations in different modeling environments. We will show results from the survey and their implications towards standardized and improved usage of snow observations in various data assimilation applications.</span></div>
Öz: Toprak nemi içeriği yeryüzünde enerji değişimi ve su döngüsü açısından çok önemli bir faktördür ve doğal risklerin değerlendirilmesi, hidroloji, ekoloji, tarım ve iklim bilimi gibi pek çok alanda büyük etkiye sahiptir. Toprak nemi özellikle arazi kullanımlarında konumsal ve zamansal olarak çok fazla değişerek çeşitli çevresel ve ekolojik sorunlara yol açabilmektedir. Bu nedenlerden dolayı, toprak nem içeriğinin konumsal değişiminin geniş ölçeklerde incelenmesi önemli bir araştırma konusudur. Sentetik Açıklıklı Radar (SAR) algılayıcıları toprak nemine duyarlı oldukları ve geniş alanları kapsadıkları için toprak neminin tespit edilmesinde önemli rol oynamaktadır. Bu çalışmada, Tarım İşletmeleri Genel Müdürlüğü Gözlü Tarım İşletmesi'nde seçilen buğday ekili ve nadasa bırakılmış tarlaların toprak nem içeriğinin konumsal ve zamansal değişiminin tam polarimetrik RADARSAT-2 görüntüleri ile belirlenebilirliğinin araştırılması amaçlanmıştır. 2016 yılı Mart ve Ekim ayları arasında yapılan aylık arazi ölçümlerinden elde edilen yersel ölçüm değerleri SAR gerisaçılım değerleri ile karşılaştırılmıştır. Çalışma sonucunda, doğrudan geri saçılım ile nem değerleri arasında -0.65 ile 0.67 arasında değişen negatif ve pozitif korelasyon katsayıları elde edilmiştir. Toprak nemi için ekili alanda buğdayın büyüme evresi olan Mayıs-Haziran döneminde daha yüksek korelasyon belirlenmiş olup, her iki alan için en iyi sonuç VV polarimetrik verisi ile elde edilmiştir.
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