Differentiating Freezing Drizzle and Freezing Rain in HRRR Model Forecasts

2021 ◽  
Author(s):  
Sarah Tessendorf ◽  
Allyson Rugg ◽  
Alexei Korolev ◽  
Ivan Heckman ◽  
Courtney Weeks ◽  
...  
Keyword(s):  
Drop Size ◽  
Prediction Models ◽  
Water Drop ◽  
Weather Prediction ◽  
Supercooled Liquid ◽  
Drop Diameter ◽  
Aircraft Icing ◽  
Freezing Rain ◽  
Primary Focus

AbstractSupercooled large drop (SLD) icing poses a unique hazard for aircraft and has resulted in new regulations regarding aircraft certification to fly in regions of known or forecast SLD icing conditions. The new regulations define two SLD icing categories based upon the maximum supercooled liquid water drop diameter (Dmax): freezing drizzle (100–500 μm) and freezing rain (> 500 μm). Recent upgrades to U.S. operational numerical weather prediction models lay a foundation to provide more relevant aircraft icing guidance including the potential to predict explicit drop size. The primary focus of this paper is to evaluate a proposed method for estimating the maximum drop size from model forecast data to differentiate freezing drizzle from freezing rain conditions. Using in-situ cloud microphysical measurements collected in icing conditions during two field campaigns between January and March 2017, this study shows that the High-Resolution Rapid Refresh model is capable of distinguishing SLD icing categories of freezing drizzle and freezing rain using a Dmax extracted from the rain category of the microphysics output. It is shown that the extracted Dmax from the model correctly predicted the observed SLD icing category as much as 99% of the time when the HRRR accurately forecast SLD conditions; however, performance varied by the method to define Dmax and by the field campaign dataset used for verification.

scholarly journals The ability of the ICE-T microphysics scheme in HARMONIE-AROME to predict aircraft icing

2021 ◽  
Author(s):  
Tim Carlsen ◽  
Morten Køltzow ◽  
Trude Storelvmo
Keyword(s):  
Water Content ◽  
Numerical Weather Prediction ◽  
Liquid Water ◽  
Prediction Models ◽  
Weather Prediction ◽  
Supercooled Liquid ◽  
Liquid Water Content ◽  
Aircraft Icing ◽  
Microphysics Scheme ◽  
Numerical Weather

Abstract In-cloud icing is a major hazard for aviation traffic and forecasting of these events is an important task for weather agencies worldwide. A common tool utilised by aviation forecasters is an icing intensity index based on supercooled liquid water from numerical weather prediction models. We seek to validate the modified microphysics scheme, ICE-T, in the HARMONIE-AROME numerical weather prediction model with respect to aircraft icing. Icing intensities and supercooled liquid water derived from two 3-month winter season simulations with the original microphysics code, CTRL, and ICE-T are compared with pilot reports of icing and satellite retrieved values of liquid and ice water content from CloudSat-CALIPSO and liquid water path from AMSR-2. The results show increased supercooled liquid water and higher icing indices in ICE-T. Several different thresholds and sizes of neighbourhood areas for icing forecasts were tested out, and ICE-T captures more of the reported icing events for all thresholds and nearly all neighbourhood areas. With a higher frequency of forecasted icing, a higher false-alarm ratio cannot be ruled out, but is not possible to quantify due to the lack of no-icing observations. The increased liquid water content in ICE-T shows a better match with the retrieved satellite observations, yet the values are still greatly underestimated at lower levels. Future studies should investigate this issue further, as liquid water content also has implications for downstream processes such as the cloud radiative effect, latent heat release, and precipitation.

scholarly journals Comparison of Snow Cover from Satellite and Numerical Weather Prediction Models in the Northern Hemisphere and Northern Europe

2009 ◽  
Vol 48 (6) ◽  
pp. 1199-1216 ◽  
Author(s):  
Otto Hyvärinen ◽  
Kalle Eerola ◽  
Niilo Siljamo ◽  
Jarkko Koskinen
Keyword(s):  
Snow Cover ◽  
Prediction Models ◽  
Weather Prediction ◽  
Ground Truth ◽  
In Situ Measurements ◽  
Lower Atmosphere ◽  
Limited Area ◽  
In Situ Observations ◽  
Using Data

Abstract Snow cover has a strong effect on the surface and lower atmosphere in NWP models. Because the progress of in situ observations has stalled, satellite-based snow analyses are becoming increasingly important. Currently, there exist several products that operationally map global or continental snow cover. In this study, satellite-based snow cover analyses from NOAA, NASA, and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), and NWP snow analyses from the High-Resolution Limited-Area Model (HIRLAM) and ECMWF, were compared using data from January to June 2006. Because no analyses were independent and since available in situ measurements were already used in the NWP analyses, no independent ground truth was available and only the consistency between analyses could be compared. Snow analyses from NOAA, NASA, and ECMWF were similar, but the analysis from NASA was greatly hampered by clouds. HIRLAM and EUMETSAT deviated most from other analyses. Even though the analysis schemes of HIRLAM and ECMWF were quite similar, the resulting snow analyses were quite dissimilar, because ECMWF used the satellite information of snow cover in the form of NOAA analyses, while HIRLAM used none. The differences are especially prominent in areas around the snow edge where few in situ observations are available. This suggests that NWP snow analyses based only on in situ measurements would greatly benefit from inclusion of satellite-based snow cover information.

scholarly journals Driftsondes: Providing In Situ Long-Duration Dropsonde Observations over Remote Regions

2013 ◽  
Vol 94 (11) ◽  
pp. 1661-1674 ◽  
Author(s):  
Stephen A. Cohn ◽  
Terry Hock ◽  
Philippe Cocquerez ◽  
Junhong Wang ◽  
Florence Rabier ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Field Experiments ◽  
Prediction Models ◽  
Weather Prediction ◽  
Lower Boundary ◽  
Remote Sensors ◽  
Long Duration ◽  
Level Data ◽  
Research Aircraft

Constellations of driftsonde systems— gondolas floating in the stratosphere and able to release dropsondes upon command— have so far been used in three major field experiments from 2006 through 2010. With them, high-quality, high-resolution, in situ atmospheric profiles were made over extended periods in regions that are otherwise very difficult to observe. The measurements have unique value for verifying and evaluating numerical weather prediction models and global data assimilation systems; they can be a valuable resource to validate data from remote sensing instruments, especially on satellites, but also airborne or ground-based remote sensors. These applications for models and remote sensors result in a powerful combination for improving data assimilation systems. Driftsondes also can support process studies in otherwise difficult locations—for example, to study factors that control the development or decay of a tropical disturbance, or to investigate the lower boundary layer over the interior Antarctic continent. The driftsonde system is now a mature and robust observing system that can be combined with flight-level data to conduct multidisciplinary research at heights well above that reached by current research aircraft. In this article we describe the development and capabilities of the driftsonde system, the exemplary science resulting from its use to date, and some future applications.

scholarly journals The S2M meteorological and snow cover reanalysis over the French mountainous areas, description and evaluation (1958–2020)

2021 ◽  
Author(s):  
Matthieu Vernay ◽  
Matthieu Lafaysse ◽  
Diego Monteiro ◽  
Pascal Hagenmuller ◽  
Rafife Nheili ◽  
...  
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Scientific Publications ◽  
Mountain Areas ◽  
Data Set ◽  
Meteorological Observations ◽  
Surface Scheme

Abstract. This work introduces the S2M (SAFRAN - SURFEX/ISBA-Crocus - MEPRA) meteorological and snow cover reanalysis in the French Alps, Pyrenees and Corsica, spanning the time period from 1958 to 2020. The simulations are made over elementary areas, referred to as massifs, designed to represent the main drivers of the spatial variability observed in mountain ranges (elevation, slope and aspect). The meteorological reanalysis is performed by the SAFRAN system, which combines information from numerical weather prediction models (ERA-40 reanalysis from 1958 to 2002, ARPEGE from 2002 to 2020) and the best possible set of available in-situ meteorological observations. SAFRAN outputs are used to drive the Crocus detailed snow cover model, which is part of the land surface scheme SURFEX/ISBA. This model chain provides simulations of the evolution of the snow cover, underlying ground, and the associated avalanche hazard using the MEPRA model. This contribution describes and discusses the main climatological characteristics (climatology, variability and trends), and the main limitations of this dataset. We provide a short overview of the scientific applications using this reanalysis in various scientific fields related to meteorological conditions and the snow cover in mountain areas. An evaluation of the skill of S2M is also displayed, in particular through comparison to 665 independent in-situ snow depth observations. Further, we describe the technical handling of this open access data set, available at this address: http://dx.doi.org/10.25326/37#v2020.2. Scientific publications using this dataset must mention in the acknowledgments: "The S2M data are provided by Météo-France - CNRS, CNRM Centre d’Etudes de la Neige, through AERIS" and refer to it as Vernay et al. (2020).

scholarly journals Intercomparison of In Situ Sensors for Ground-Based Land Surface Temperature Measurements

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5268
Author(s):  
Praveena Krishnan ◽  
Tilden P. Meyers ◽  
Simon J. Hook ◽  
Mark Heuer ◽  
David Senn ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Field Experiments ◽  
Prediction Models ◽  
Weather Prediction ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Oak Ridge

Land surface temperature (LST) is a key variable in the determination of land surface energy exchange processes from local to global scales. Accurate ground measurements of LST are necessary for a number of applications including validation of satellite LST products or improvement of both climate and numerical weather prediction models. With the objective of assessing the quality of in situ measurements of LST and to evaluate the quantitative uncertainties in the ground-based LST measurements, intensive field experiments were conducted at NOAA’s Air Resources Laboratory (ARL)’s Atmospheric Turbulence and Diffusion Division (ATDD) in Oak Ridge, Tennessee, USA, from October 2015 to January 2016. The results of the comparison of LSTs retrieved by three narrow angle broadband infrared temperature sensors (IRT), hemispherical longwave radiation (LWR) measurements by pyrgeometers, forward looking infrared camera with direct LSTs by multiple thermocouples (TC), and near surface air temperature (AT) are presented here. The brightness temperature (BT) measurements by the IRTs agreed well with a bias of <0.23 °C, and root mean square error (RMSE) of <0.36 °C. The daytime LST(TC) and LST(IRT) showed better agreement (bias = 0.26 °C and RMSE = 0.67 °C) than with LST(LWR) (bias > 1.1 and RMSE > 1.46 °C). In contrast, the difference between nighttime LSTs by IRTs, TCs, and LWR were <0.47 °C, whereas nighttime AT explained >81% of the variance in LST(IRT) with a bias of 2.64 °C and RMSE of 3.6 °C. To evaluate the annual and seasonal differences in LST(IRT), LST(LWR) and AT, the analysis was extended to four grassland sites in the USA. For the annual dataset of LST, the bias between LST (IRT) and LST (LWR) was <0.7 °C, except at the semiarid grassland (1.5 °C), whereas the absolute bias between AT and LST at the four sites were <2 °C. The monthly difference between LST (IRT) and LST (LWR) (or AT) reached up to 2 °C (5 °C), whereas half-hourly differences between LSTs and AT were several degrees in magnitude depending on the site characteristics, time of the day and the season.

FROST-2014: The Sochi Winter Olympics International Project

2017 ◽  
Vol 98 (9) ◽  
pp. 1908-1929 ◽  
Author(s):  
Dmitry Kiktev ◽  
Paul Joe ◽  
George A. Isaac ◽  
Andrea Montani ◽  
Inger-Lise Frogner ◽  
...  
Keyword(s):  
Prediction Models ◽  
Deep Convection ◽  
Weather Prediction ◽  
Ensemble Prediction ◽  
Winter Olympics ◽  
Systems Model ◽  
Weather Observations ◽  
Prediction Systems ◽  
Development And Validation

Abstract The World Meteorological Organization (WMO) World Weather Research Programme’s (WWRP) Forecast and Research in the Olympic Sochi Testbed program (FROST-2014) was aimed at the advancement and demonstration of state-of-the-art nowcasting and short-range forecasting systems for winter conditions in mountainous terrain. The project field campaign was held during the 2014 XXII Olympic and XI Paralympic Winter Games and preceding test events in Sochi, Russia. An enhanced network of in situ and remote sensing observations supported weather predictions and their verification. Six nowcasting systems (model based, radar tracking, and combined nowcasting systems), nine deterministic mesoscale numerical weather prediction models (with grid spacings down to 250 m), and six ensemble prediction systems (including two with explicitly simulated deep convection) participated in FROST-2014. The project provided forecast input for the meteorological support of the Sochi Olympic Games. The FROST-2014 archive of winter weather observations and forecasts is a valuable information resource for mesoscale predictability studies as well as for the development and validation of nowcasting and forecasting systems in complex terrain. The resulting innovative technologies, exchange of experience, and professional developments contributed to the success of the Olympics and left a post-Olympic legacy.

scholarly journals The International Soil Moisture Network: an open-source data hosting facility in support of meteorology and climate science

2021 ◽  
Author(s):  
Ivana Petrakovic ◽  
Irene Himmelbauer ◽  
Daniel Aberer ◽  
Lukas Schremmer ◽  
Philippe Goryl ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
International Cooperation ◽  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Source Surface ◽  
Federal Institute

&lt;p&gt;The International Soil Moisture Network (ISMN, https://ismn.earth) is international cooperation to establish and maintain a unique centralized global data hosting facility, making in-situ soil moisture data easily and freely accessible (Dorigo et al., 2021). Initiated in 2009 as a community effort through international cooperation (ESA, GEWEX, GTN-H, GCOS, TOPC, HSAF, QA4SM, C3S, etc.), the ISMN is an essential means for validating and improving global satellite soil moisture products, land surface-, climate-, and hydrological models.&amp;#160;&lt;br&gt;&lt;br&gt;The ISMN is a widely used, reliable, and consistent in-situ data source (surface and sub-surface) collected by a myriad of data organizations on a voluntary basis. &amp;#160;The in-situ soil moisture measurements are collected, harmonized in terms of units and sampling rates, advanced quality control is applied and the data is then stored in a database and made available online, where users can download it for free. Currently, 71 networks are participating with more than 2800 stations distributed on a global scale and a steadily increasing number of user communities. Long term time series with mainly hourly timestamps from 1952 &amp;#8211; up to near-real-time are stored in the database, including daily near-real-time updates. Besides soil moisture in our database are stored other meteorological variables as well (air temperature, soil temperature, precipitation, snow depth, etc.).&lt;br&gt;&lt;br&gt;The ISMN provides benchmark data for several operational services such as ESA CCI Soil Moisture, the Copernicus Climate Change (C3S) and Global Land Service (CGLS), and the online validation tool QA4SM. ISMN data is widely used in a variety of scientific fields (e.g., climate, water, agriculture, disasters, ecosystems, weather, biodiversity, etc).&lt;br&gt;&lt;br&gt;To validate the land surface representations of meteorological forecasting models soil moisture from the ISMN has often been used. The development of various generations of TESSEL models used both in the Integrated Forecasting Systems and reanalysis products of ECMWF, greatly profited from soil moisture and temperature data from the ISMN. Using ISMN data several studies assessed the soil moisture skill of the Weather Research and Forecasting Model (WRF) and assessed the forecast skill or new implementations of numerical weather prediction models.&lt;br&gt;&lt;br&gt;We greatly acknowledge the financial support provided by ESA through various projects: SMOSnet International Soil Moisture Network, IDEAS+, and QA4EO.&lt;br&gt;&lt;br&gt;To ensure a long-term funding for the ISMN operations, several ideas were perused together with ESA. A partner for this task could be found within the International Center for Water Resources and Global Change (ICWRGC) hosted by the German Federal Institute of Hydrology (BfG).&amp;#160;&lt;br&gt;&lt;br&gt;In this session, we want to give an overview and future outlook of the ISMN, highlighting its unique features and discuss challenges in supporting the hydrological research community in need of freely available, standardized, and quality-controlled datasets.&amp;#160;&lt;/p&gt;

scholarly journals Southern Ocean Cloud and Aerosol data: a compilation of measurements from the 2018 Southern Ocean Ross Sea Marine Ecosystems and Environment voyage

2020 ◽  
Author(s):  
Stefanie Kremser ◽  
Mike Harvey ◽  
Peter Kuma ◽  
Sean Hartery ◽  
Alexia Saint-Macary ◽  
...  
Keyword(s):  
Southern Ocean ◽  
Climate Models ◽  
Dimethyl Sulfide ◽  
Prediction Models ◽  
Climate Model ◽  
Ross Sea ◽  
Evaluation Studies ◽  
Weather Prediction ◽  
Data Sets

Abstract. Due to its remote location and extreme weather conditions, atmospheric in situ measurements are rare in the Southern Ocean. As a result, aerosol-cloud interactions in this region are poorly understood and remain a major source of uncertainty in climate models. This, in turn, contributes substantially to persistent biases in climate model simulations, numerical weather prediction models and reanalyses. It has been shown in previous studies that in situ and ground-based remote sensing measurements across the Southern Ocean are critical for complementing satellite data sets due to the importance of boundary layer and low-level cloud processes. These processes are poorly sampled by satellite-based measurements which are typically obscured by near-continuous overlying cloud cover observed in this region. In this work we present a comprehensive set of ship-based aerosol and meteorological observations collected on the TAN1802 voyage of R/V Tangaroa across the Southern Ocean, from Wellington, New Zealand, to the Ross Sea, Antarctica. The voyage was carried out from 8 February to 21 March, 2018. Many distinct, but contemporaneous, data sets were collected throughout the voyage. The compiled data sets include measurements from a range of instruments, such as (i) meteorological conditions at the sea surface and profile measurements; (ii) the size and concentration of particles; (iii) trace gases dissolved in the ocean surface such as dimethyl sulfide and carbonyl sulfide; (iv) and remotely sensed observations of low clouds. Here, we describe the voyage, the instruments, data processing, and provide a brief overview of some of the data products available. We encourage the scientific community to use these measurements for further analysis and model evaluation studies, in particular, for studies of Southern Ocean clouds, aerosol and their interaction. The data sets presented in this study are publicly available at https://doi.org/10.5281/zenodo.4060237 (Kremser et al. 2020).

scholarly journals In-situ observational network for extreme weather events in India

MAUSAM ◽  
2021 ◽  
Vol 67 (1) ◽  
pp. 67-76
Author(s):  
GAJENDRA KUMAR ◽  
SURESH CHAND ◽  
R. R. MALI ◽  
S. K. KUNDU ◽  
A. K. BAXLA
Keyword(s):  
High Performance ◽  
Prediction Models ◽  
Weather Prediction ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Natural Systems ◽  
Observational Network ◽  
Weather Events ◽  
Vegetables And Fruits

Extreme weather events, interacting with vulnerable human and natural systems, can lead to disasters, especially in absence of responsive social system. Accurate and timely monitoring and forecast of heavy rains, tropical cyclones, thunderstorms, hailstorms, cloudburst, drought, heat and cold waves, etc. are required to respond effectively to such events. Due to extreme weather events, crops over large parts of the country are adversely affected reducing production of total food grains, fodder, cash crops, vegetables and fruits which in turn affect the earnings and livelihood of individual farmers as well as the economy of the country. In situ observational network are the vital component for skilful prediction of extreme weather events. Current observational requirements for extreme weather prediction are met, to varying degrees by a range of in-situ observing systems and space-based systems. The augmentation of in-situ observational network is continuously progressing. IMD now has a network of Doppler Weather Radars (DWRs), Automatic Weather Stations (AWSs), Agro AWSs, Automatic Rain Gauges (ARGs), GPS upper air systems etc. These observations along with non-conventional (satellite) data are now being used to run its global and regional numerical prediction models on High Performance Computing Systems (HPCS). This has improved monitoring and forecasting capabilities for extreme weather events like cyclones, severe thunderstorm, heavy rainfall and floods in a significant manner. This paper provides an overview of the role of in-situ observational network for extreme weather events in India, framework for further augmentation to the network and other requirements to further enhance capabilities for high impact & extreme weather events and natural hazards.

scholarly journals Assessment of the accuracy of snow water equivalent calculation with the use of global numerical weather prediction models and SnoWE snowpack model (by the example of the Kama River basin)

2021 ◽  
Vol 66 (1) ◽  
Author(s):  
Andrey N. Shikhov ◽  
◽  
Evgenii V. Churiulin ◽  
Rinat K. Abdullin ◽  
◽  
...  
Keyword(s):  
River Basin ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Entire Area ◽  
Numerical Weather ◽  
In Situ Data ◽  
Snow Water

The paper discusses the results of snow cover formation and snowmelt modeling in the Kama river basin (S = 507 km2) using two approaches previously developed by the authors. The first one is the SnoWE snowpack model developed at the Hydrometeorological Center of the Russian Federation and used in quasi-operational mode since 2015, and the second is GIS-based empirical technique which was previously implemented for the Kama river basin. Both methods are based on a combination of numerical weather prediction (NWP) models data with operational synoptic observations at the weather stations. The study was performed for the winter seasons 2018/19 and 2019/20. To assess the reliability of simulated snow water equivalent (SWE), we obtained in-situ data from 68 locations (snow survey routes) distributed over the entire area of ​​the river basin. As a result of the study, the main advantages and limitations of two methods for SWE calculation were identified. As for the maximum values of SWE, the root mean square error (RMSE) of simulated SWE ranges from 14% to 28% of the average observed SWE according to in-situ data. It was found, that the SnoWE model more reliably reproduces SWE in the lowland part of the river basin. Simultaneously, SWE was substantially underestimated according to the SnoWE model in the northern and mountainous parts of the basin,. The second method provides a more realistic estimate of the spatial distribution of SWE over the area, as well as a higher accuracy of calculation for its northern part of the river basin. The main drawback of the method is the substantial overestimation of the intensity of snowmelt and snow sublimation. Consequently, the accuracy of SWE calculations sharply decreases in the spring season. Wherein, SWE calculation accuracy in the winter season 2019/20 was substantially lower than in 2018/19 due to frequent thaws.

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