Comparison of cloud boundaries measured with 8.6 mm radar and 10.6 micrometer lidar

One of the most basic cloud properties is location; the height of cloud base and the height of cloud top. The glossary of meteorology defines cloud base (top) as follows: 'For a given cloud or cloud layer, that lowest (highest) level in the atmosphere at which the air contains a perceptible quantity of cloud particles.' Our studies show that for a 8.66 mm radar, and a 10.6 micrometer lidar, the level at which cloud hydrometers become 'perceptible' can vary significantly as a function of the different wavelengths, powers, beamwidths and sampling rates of the two remote sensors

Comparison of cloud microphysical parameters derived from surface and satellite measurements during FIRE phase 2

Cloud microphysical properties are an important component in climate model parameterizations of water transport, cloud radiative exchange, and latent heat processes. Estimation of effective cloud particle size, liquid or ice water content, and optical depth from satellite-based instrumentation is needed to develop a climatology of cloud microphysical properties and to better understand and model cloud processes in atmospheric circulation. These parameters are estimated from two different surface data sets taken at Coffeyville, Kansas, during the First ISCCP Regional Experiment (FIRE) Phase-2 Intensive Field Observation (IFO) period (November 13 - December 7, 1991). Satellite data can also provide information about optical depth and effective particle size. This paper explores the combination of the FIRE-2 surface and satellite data to determine each of the cloud microphysical properties

Remote sensing data from CLARET: A prototype CART data set

The data set containing radiation, meteorological , and cloud sensor observations is documented. It was prepared for use by the Department of Energy's Atmospheric Radiation Measurement (ARM) Program and other interested scientists. These data are a precursor of the types of data that ARM Cloud And Radiation Testbed (CART) sites will provide. The data are from the Cloud Lidar And Radar Exploratory Test (CLARET) conducted by the Wave Propagation Laboratory during autumn 1989 in the Denver-Boulder area of Colorado primarily for the purpose of developing new cloud-sensing techniques on cirrus. After becoming aware of the experiment, ARM scientists requested archival of subsets of the data to assist in the developing ARM program. Five CLARET cases were selected: two with cirrus, one with stratus, one with mixed-phase clouds, and one with clear skies. Satellite data from the stratus case and one cirrus case were analyzed for statistics on cloud cover and top height. The main body of the selected data are available on diskette from the Wave Propagation Laboratory or Los Alamos National Laboratory

Results of the first Arctic Heat Open Science Experiment

Author Posting. © American Meteorological Society, 2018. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 99 (2018): 513-520, doi:10.1175/BAMS-D-16-0323.1.Seasonally ice-covered marginal seas are among the most difficult regions in the Arctic to study. Physical constraints imposed by the variable presence of sea ice in all stages of growth and melt make the upper water column and air–sea ice interface especially challenging to observe. At the same time, the flow of solar energy through Alaska’s marginal seas is one of the most important regulators of their weather and climate, sea ice cover, and ecosystems. The deficiency of observing systems in these areas hampers forecast services in the region and is a major contributor to large uncertainties in modeling and related climate projections. The Arctic Heat Open Science Experiment strives to fill this observation gap with an array of innovative autonomous floats and other near-real-time weather and ocean sensing systems. These capabilities allow continuous monitoring of the seasonally evolving state of the Chukchi Sea, including its heat content. Data collected by this project are distributed in near–real time on project websites and on the Global Telecommunications System (GTS), with the objectives of (i) providing timely delivery of observations for use in weather and sea ice forecasts, for model, and for reanalysis applications and (ii) supporting ongoing research activities across disciplines. This research supports improved forecast services that protect and enhance the safety and economic viability of maritime and coastal community activities in Alaska. Data are free and open to all (see www.pmel.noaa.gov/arctic-heat/).This work was supported by NOAA Ocean and Atmospheric Research and the Joint Institute for the Study of the Atmosphere and Ocean (JISAO) under NOAA Cooperative Agreement NA15OAR4320063 and by the Innovative Technology for Arctic Exploration (ITAE) program at JISAO/PMEL. Jayne, Robbins, and Ekholm were supported by ONR (N00014-12-10110)

Measurements from the University of Colorado RAAVEN Uncrewed Aircraft System during ATOMIC

Between 24&nbsp;January and 15&nbsp;February 2020, small uncrewed aircraft systems (sUASs) were deployed to Morgan Lewis (Barbados) as part of the Atlantic Tradewind Ocean&ndash;Atmosphere Mesoscale Interaction Campaign (ATOMIC), a sister project to the ElUcidating the RolE of Cloud-Circulation Coupling in ClimAte (EUREC4A) project. The observations from ATOMIC and EUREC4A were aimed at improving our understanding of trade-wind cumulus clouds and the environmental regimes supporting them and involved the deployment of a wide variety of observational assets, including aircraft, ships, surface-based systems, and profilers. The current paper describes ATOMIC observations obtained using the University of Colorado Boulder RAAVEN (Robust Autonomous Aerial Vehicle&nbsp;&ndash; Endurant Nimble) sUAS. This platform collected nearly 80&thinsp;h of data throughout the lowest kilometer of the atmosphere, sampling the near-shore environment upwind from Barbados. Data from these platforms are publicly available through the National Oceanic and Atmospheric Administration's National Center for Environmental Intelligence (NCEI) archive. The primary DOI for the quality-controlled dataset described in this paper is https://doi.org/10.25921/jhnd-8e58 (de Boer et al., 2021). &nbsp;</p

A Framework for the Development, Design and Implementation of a Sustained Arctic Ocean Observing System

Rapid Arctic warming drives profound change in the marine environment that have significant socio-economic impacts within the Arctic and beyond, including climate and weather hazards, food security, transportation, infrastructure planning and resource extraction. These concerns drive efforts to understand and predict Arctic environmental change and motivate development of an Arctic Region Component of the Global Ocean Observing System (ARCGOOS) capable of collecting the broad, sustained observations needed to support these endeavors. This paper provides a roadmap for establishing the ARCGOOS. ARCGOOS development must be underpinned by a broadly-endorsed framework grounded in high-level policy drivers and the scientific and operational objectives that stem from them. This should be guided by a transparent, internationally accepted governance structure with recognized authority and organizational relationships with the national agencies that ultimately execute network plans. A governance model for ARCGOOS must guide selection of objectives, assess performance and fitness-to-purpose, and advocate for resources. A requirements-based framework for an ARCGOOS begins with the Societal Benefit Areas (SBAs) that underpin the system. SBAs motivate investments and define the system's science and operational objectives. Objectives can then be used to identify key observables and their scope. The domains of planning/policy, strategy, and tactics define scope ranging from decades and basins to focused observing with near real time data delivery. Patterns emerge when this analysis is integrated across an appropriate set of SBAs and science/operational objectives, identifying impactful variables and the scope of the measurements. When weighted for technological readiness and logistical feasibility, this can be used to select Essential ARCGOOS Variables, analogous to Essential Ocean Variables of the Global Ocean Observing System. The Arctic presents distinct needs and challenges, demanding novel observing strategies. Cost, traceability and ability to integrate region-specific knowledge have to be balanced, in an approach that builds on existing and new observing infrastructure. ARCGOOS should benefit from established data infrastructures following the Findable, Accessible, Interoperable, Reuseable Principles to ensure preservation and sharing of data and derived products. Linking to the Sustaining Arctic Observing Networks (SAON) process and involving Arctic stakeholders, for example through liaison with the International Arctic Science Committee (IASC), can help ensure success

Cancer data quality and harmonization in Europe: the experience of the BENCHISTA Project – international benchmarking of childhood cancer survival by stage

IntroductionVariation in stage at diagnosis of childhood cancers (CC) may explain differences in survival rates observed across geographical regions. The BENCHISTA project aims to understand these differences and to encourage the application of the Toronto Staging Guidelines (TG) by Population-Based Cancer Registries (PBCRs) to the most common solid paediatric cancers.MethodsPBCRs within and outside Europe were invited to participate and identify all cases of Neuroblastoma, Wilms Tumour, Medulloblastoma, Ewing Sarcoma, Rhabdomyosarcoma and Osteosarcoma diagnosed in a consecutive three-year period (2014-2017) and apply TG at diagnosis. Other non-stage prognostic factors, treatment, progression/recurrence, and cause of death information were collected as optional variables. A minimum of three-year follow-up was required. To standardise TG application by PBCRs, on-line workshops led by six tumour-specific clinical experts were held. To understand the role of data availability and quality, a survey focused on data collection/sharing processes and a quality assurance exercise were generated. To support data harmonization and query resolution a dedicated email and a question-and-answers bank were created.Results67 PBCRs from 28 countries participated and provided a maximally de-personalized, patient-level dataset. For 26 PBCRs, data format and ethical approval obtained by the two sponsoring institutions (UCL and INT) was sufficient for data sharing. 41 participating PBCRs required a Data Transfer Agreement (DTA) to comply with data protection regulations. Due to heterogeneity found in legal aspects, 18 months were spent on finalizing the DTA. The data collection survey was answered by 68 respondents from 63 PBCRs; 44% of them confirmed the ability to re-consult a clinician in cases where stage ascertainment was difficult/uncertain. Of the total participating PBCRs, 75% completed the staging quality assurance exercise, with a median correct answer proportion of 92% [range: 70% (rhabdomyosarcoma) to 100% (Wilms tumour)].ConclusionDifferences in interpretation and processes required to harmonize general data protection regulations across countries were encountered causing delays in data transfer. Despite challenges, the BENCHISTA Project has established a large collaboration between PBCRs and clinicians to collect detailed and standardised TG at a population-level enhancing the understanding of the reasons for variation in overall survival rates for CC, stimulate research and improve national/regional child health plans

Using Radar, Lidar and Radiometer Data from NSA and SHEBA to Quantify Cloud Property Effects on the Surface Heat Budget in the Arctic

Cloud and radiation data from two distinctly different Arctic areas are analyzed to study the differences between coastal Alaskan and open Arctic Ocean region clouds and their respective influence on the surface radiation budget. The cloud and radiation datasets were obtained from (1) the DOE North Slope of Alaska (NSA) facility in the coastal town of Barrow, Alaska, and (2) the SHEBA field program, which was conducted from an icebreaker frozen in, and drifting with, the sea-ice for one year in the Western Arctic Ocean. Radar, lidar, radiometer, and sounding measurements from both locations were used to produce annual cycles of cloud occurrence and height, atmospheric temperature and humidity, surface longwave and shortwave broadband fluxes, surface albedo, and cloud radiative forcing. In general, both regions revealed a similar annual trend of cloud occurrence fraction with minimum values in winter (60-75%) and maximum values during spring, summer and fall (80-90%). However, the annual average cloud occurrence fraction for SHEBA (76%) was lower than the 6-year average cloud occurrence at NSA (92%). Both Arctic areas also showed similar annual cycle trends of cloud forcing with clouds warming the surface through most of the year and a period of surface cooling during the summer, when cloud shading effects overwhelm cloud greenhouse effects. The greatest difference between the two regions was observed in the magnitude of the cloud cooling effect (i.e., shortwave cloud forcing), which was significantly stronger at NSA and lasted for a longer period of time than at SHEBA. This is predominantly due to the longer and stronger melt season at NSA (i.e., albedo values that are much lower coupled with Sun angles that are somewhat higher) than the melt season observed over the ice pack at SHEBA. Longwave cloud forcing values were comparable between the two sites indicating a general similarity in cloudiness and atmospheric temperature and humidity structure between the two regions

The NSA/SHEBA Cloud & Radiation Comparison Study

Cloud and radiation data from two distinctly different Arctic areas are analyzed to study the differences between coastal Alaskan and open Arctic Ocean region clouds and their respective influence on the surface radiation budget. The cloud and radiation datasets were obtained from 1) the DOE North Slope of Alaska (NSA) facility in the coastal town of Barrow, Alaska, and 2) the SHEBA field program, which was conducted from an icebreaker frozen in, and drifting with, the sea-ice for one year in the Western Arctic Ocean. Radar, lidar, radiometer, and sounding measurements from both locations were used to produce annual cycles of cloud occurrence and height, atmospheric temperature and humidity, surface longwave and shortwave broadband fluxes, surface albedo, and cloud radiative forcing. In general, both regions revealed a similar annual trend of cloud occurrence fraction with minimum values in winter (60-75%) and maximum values during spring, summer and fall (80-90%). However, the annual average cloud occurrence fraction for SHEBA (76%) was lower than the 6-year average cloud occurrence at NSA (92%). Both Arctic areas also showed similar annual cycle trends of cloud forcing with clouds warming the surface through most of the year and a period of surface cooling during the summer, when cloud shading effects overwhelm cloud greenhouse effects. The greatest difference between the two regions was observed in the magnitude of the cloud cooling effect (i.e., shortwave cloud forcing), which was significantly stronger at NSA and lasted for a longer period of time than at SHEBA. This is predominantly due to the longer and stronger melt season at NSA (i.e., albedo values that are much lower coupled with Sun angles that are somewhat higher) than the melt season observed over the ice pack at SHEBA. Longwave cloud forcing values were comparable between the two sites indicating a general similarity in cloudiness and atmospheric temperature and humidity structure between the two regions

NOAA PSL Soil Moisture and Surface Temperature Probe Data for SPLASH

&lt;p&gt;This dataset contains measurements from a hand-held FieldScout TDR Soil Moisture Meter within the 0-10 cm soil depth of: Time (UTC), GPS locations, Electrical Conductivity (EC), compensated percent volumetric water content (VWC), soil surface temperature (T), and rod length (inches) obtained during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA).&nbsp; These data were collected around the SPLASH campaign areas near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from between June 1st, 2022 and September 18th, 2023, under support from the NOAA Physical Sciences Laboratory and NOAA Weather Program Office under award NA21OAR4590363.&lt;/p&gt;&lt;p&gt;Two file formats are provided: one version is text csv format and the second version is in NetCDF.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Volumetric water content calculations:&nbsp;&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Data were calibrated and adjusted, with a soil-specific sample set, to improve accuracy and compensate for the meter's default "standard" soil type used in the sampling.&nbsp; VWC data was&nbsp;correlated by measuring the weight of a known volume of soil from a range of saturation values. Samples were measured and weighed, dried at 105 degrees C for 48 hours, then weighed again. Calculations of VWC (VWC&lt;strong&gt; &lt;/strong&gt;= 100*(Mwet - Mdry)/(w*Vtot) )were plotted against TDR readings.&nbsp; Where:&nbsp;&lt;/p&gt;&lt;p&gt;Mwet, Mdry = mass (g) of wet and dry soil respectively&nbsp;&lt;/p&gt;&lt;p&gt;Vtot = total soil volume (ml)&nbsp;&lt;/p&gt;&lt;p&gt;w = density of water (1g/ml)&nbsp;&lt;/p&gt;&lt;p&gt;A regression analysis&nbsp; to correlate TDR readings to the samples is below and was applied to the dataset.&lt;/p&gt;&lt;p&gt;vwc_calculated = vwc_probe * slope + intercept&lt;/p&gt;&lt;p&gt;slope = 1.20665, intercept = 0.0837017 m3/m3, slope_std_error = 0.09229, intercept_std_error = 0.0217403 m3/m3&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Definitions:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;TDR (Time Domain Reflectometry): A technique for measuring soil moisture content that uses the fact that water has a much higher dielectric permittivity than air, soil minerals, and organic matter.&nbsp;&lt;/p&gt;&lt;p&gt;VWC (Volumetric Water Content): The ratio of the volume of water in a given volume of soil to the total soil volume expressed as a decimal or a percentage. The percent of the soil volume that is filled with water. At saturation, the VWC will equal the soil porosity (Saturation is typically around 50%).&lt;/p&gt;&lt;p&gt;EC (Electrical Conductivity): A measure of how well the soil solution conducts electricity. The EC is influenced by the amount of salt and water in the soil.&nbsp;&lt;/p&gt;&lt;p&gt;The VWC measured by TDR is an average over the length of the waveguide.&nbsp;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Soil Characteristics:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Soil at both Kettle Ponds (KEP1 and KPA) locations and Avery Picnic (AYP) were lab tested for composition as follows:&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Sample ID &nbsp; &nbsp; &nbsp; Depth(in.) &nbsp; &nbsp; &nbsp; Sand(%) &nbsp; &nbsp; Silt(%) &nbsp; &nbsp; Clay(%) &nbsp; &nbsp; Soil Texture&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;------------------------------------------------------------------------------------------------------ &nbsp;&lt;/p&gt;&lt;p&gt;KEP1 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 2 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;43 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;35 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;22 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Loam&lt;/p&gt;&lt;p&gt;AYP &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 2 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;40 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;35 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;25 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Loam&lt;/p&gt;&lt;p&gt;KPA &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 2 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;35 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;42 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;22 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Loam&lt;/p&gt;&lt;p&gt;------------------------------------------------------------------------------------------------------&lt;/p&gt