[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4307":3,"related-4307":76},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":24,"classification":25,"search_phrases":41,"slug":44,"view_count":15,"doi":45,"paper":46,"created_at":75},4307,"Evaluating the scanning capabilities of an MWR to estimate temperature and humidity advection from a single site","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fegusphere-2026-4994","Abstract. Advection plays a fundamental role in the transport of heat and moisture within the Atmospheric Boundary Layer (ABL). Recently, horizontal advection has been quantified using ground-based remote sensing networks deployed at multiple locations. This study presents a novel single-site methodology to estimate horizontal temperature and humidity advection. Horizontal gradients are derived from a microwave radiometer (MWR) by exploiting its azimuthal scanning capability, while wind components are obtained from a Doppler wind lidar (DWL). Advection is estimated and a comprehensive uncertainty quantification framework is developed, based on standard error propagation and a Monte Carlo approach that accounts for correlated uncertainties in the MWR's observations. The resulting uncertainties are evaluated relative to the magnitude of advection over a range of horizontal gradients and wind speeds. For typical ABL conditions, the uncertainty associated with humidity advection is approximately 60 % of its estimated value, while temperature advection uncertainties reach 140 %. However, under conditions characterized by strong horizontal gradients and high wind speeds, the observed magnitude of advection exceeds its uncertainties, enabling the detection of significant events such as frontal passages. As proof of concept, two strong advection episodes are analyzed. On 19 June 2022, a pronounced cold advection event was observed, and the current estimation captured intense cooling throughout the ABL, with vertically averaged values reaching −10 K h-1, with relative uncertainties generally below 40 %. A second case, on 8 October 2021, highlights humidity advection, with dominant zonal contribution at around 2300 UTC. The height-averaged estimated humidity advection reached -10 g kg-1 h-1, with uncertainties typically below 30 %. The present study provides a novel advection estimate and its robust assessment of capabilities and limitations, highlighting the clear identification of strong advection events in the ABL, reducing the logistic complexity of capturing this variable.","摘要：平流在大气边界层（ABL）内热量和水汽的输送中起着基础性作用。近年来，研究者利用部署在多个地点的地基遥感网络对水平平流进行了量化。本研究提出了一种新颖的单站点方法，用于估算水平温度平流和湿度平流。水平梯度通过利用微波辐射计（MWR）的方位扫描能力获得，而风分量则由多普勒测风激光雷达（DWL）获取。研究估算了平流，并基于标准误差传播和蒙特卡洛方法建立了全面的不确定性量化框架，该框架考虑了MWR观测中相关的不确定性。所得不确定性在一系列水平梯度和风速条件下相对于平流量级进行了评估。在典型的大气边界层条件下，湿度平流的不确定性约为其估算值的60%，而温度平流的不确定性可达140%。然而，在强水平梯度和高风速条件下，观测到的平流量级超过其不确定性，从而能够探测到锋面过境等重要事件。作为概念验证，研究分析了两次强平流事件。2022年6月19日观测到一次显著的冷平流事件，当前估算方法捕捉到了整个大气边界层内的强烈降温，垂直平均值达到−10 K h⁻¹，相对不确定性通常低于40%。第二个案例为2021年10月8日，突出了湿度平流，在UTC 2300左右以纬向贡献为主。高度平均的估算湿度平流达到−10 g kg⁻¹ h⁻¹，不确定性通常低于30%。本研究提供了一种新颖的平流估算方法及其对能力和局限性的稳健评估，突出了对大气边界层中强平流事件的清晰识别，降低了捕捉该变量的后勤复杂性。",null,"OpenAlex","2026-10-05T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文研究微波辐射计扫描估算大气边界层温湿平流，属大气遥感基础研究，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23],"微波辐射计","大气边界层","遥感观测",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":29},"needs_review","2026-10-22.2","rules",{"candidates":30,"agricultural_evidence":14,"reason":40},[31],{"code":32,"score":33,"supported":34,"title":35,"abstract":36,"tags":39},"I3",6,true,[],[37,38],"遥感","remote sensing",[],"农业相关性需要复核",[42,43],"微波辐射计 平流观测","多普勒测风激光雷达 温湿平流","微波辐射计平流观测-4307","10.5194\u002Fegusphere-2026-4994",{"doi":45,"openalex_id":47,"authors":48,"venue":9,"cited_by_count":15,"oa_url":67,"card":68,"direction":72,"ingested_from":74},"W7220362247",[49,52,55,58,61,64],{"name":50,"orcid":51},"Andrea Burgos-Cuevas","https:\u002F\u002Forcid.org\u002F0000-0003-2012-4839",{"name":53,"orcid":54},"David D. Turner","https:\u002F\u002Forcid.org\u002F0000-0003-1097-897X",{"name":56,"orcid":57},"Jan H. Schween","https:\u002F\u002Forcid.org\u002F0000-0001-6686-1207",{"name":59,"orcid":60},"Tobias Marke","https:\u002F\u002Forcid.org\u002F0000-0001-7804-9056",{"name":62,"orcid":63},"Tobias Böck","https:\u002F\u002Forcid.org\u002F0009-0006-0750-9274",{"name":65,"orcid":66},"Ulrich Löhnert","https:\u002F\u002Forcid.org\u002F0000-0002-9023-0269","https:\u002F\u002Fegusphere.copernicus.org\u002Fpreprints\u002F2026\u002Fegusphere-2026-4994\u002Fegusphere-2026-4994.pdf",{"tldr":69,"method":70,"finding":71,"direction":72,"opportunity":73},"提出单站点微波辐射计扫描估算水平温湿平流的新方法并量化其不确定性。","微波辐射计方位扫描获取水平梯度，多普勒测风激光雷达测风，蒙特卡洛误差传播。","温湿平流不确定度分别达140%和60%，但强梯度大风速下可识别锋面等强平流事件。","农业遥感与作物表型","可将单站平流估算用于农田边界层水热输送监测，降低多站组网成本，服务精准灌溉与蒸散研究。","openalex","2026-10-06T23:30:19.865558Z",{"total":15,"page":77,"page_size":77,"items":78},1,[]]