scholarly journals Predicting the Snow-to-Liquid Ratio of Surface Precipitation Using a Bulk Microphysics Scheme

2012 ◽  
Vol 140 (8) ◽  
pp. 2461-2476 ◽  
Author(s):  
J. A. Milbrandt ◽  
A. Glazer ◽  
D. Jacob
Keyword(s):  
Subjective Evaluation ◽  
Weather Prediction ◽  
Total Solid ◽  
Liquid Ratio ◽  
Microphysics Scheme ◽  
New Approach ◽  
Surface Precipitation ◽  
Precipitation Forecasting ◽  
Nwp Model ◽  
Crystal Aggregate

Abstract Bulk microphysics parameterizations play an increasingly important role for quantitative precipitation forecasting (QPF) in operational numerical weather prediction (NWP). For wintertime, numerical prediction of snowfall amounts is done by applying an estimated snow-to-liquid ratio to the liquid-equivalent QPF from the NWP model. A method has been developed to use prognostic fields from a detailed bulk scheme to predict the instantaneous snow-to-liquid ratio of precipitating snow. By exploiting aspects of the parameterization of the large crystal/aggregate (snow) category, which allow for a prediction of the mean particle size and a corresponding realistic bulk density, combined with pristine ice and graupel fields, the total volume flux of ice-phase precipitation (excluding hail) is computed, independently from the computation of the total solid mass flux. Ultimately, the accumulated unmelted solid precipitation quantity is thus predicted without having to estimate the average snow-to-liquid ratio for a given event, as is typically done for wintertime QPF. The new technique has been implemented into the two-moment version of the Milbrandt–Yau microphysics scheme, which was used in a high-resolution (2.5 and 1 km) NWP modeling system over the Vancouver–Whistler region of Canada in support of forecasting for the Vancouver 2010 Olympic and Paralympic Games. Experimental fields were produced including the instantaneous snow-to-liquid ratio and the snowfall accumulation predicted directly from the scheme using the new approach. Subjective evaluation indicates that the model can discriminate between low-density and high-density snow for instantaneous precipitation. Comparison of the predicted snow-to-liquid ratio to observed climatologies indicates that the scheme produces a realistic probability distribution.

Impact of Environmental Instability on Convective Precipitation Uncertainty Associated with the Nature of the Rimed Ice Species in a Bulk Microphysics Scheme

2013 ◽  
Vol 141 (8) ◽  
pp. 2841-2849 ◽  
Author(s):  
Kwinten Van Weverberg
Keyword(s):  
Environmental Conditions ◽  
Deep Convection ◽  
Weather Prediction ◽  
Convective Precipitation ◽  
Microphysics Scheme ◽  
Surface Precipitation ◽  
Squall Lines ◽  
Environmental Instability ◽  
Cloud Modeling ◽  
The Impact

Abstract Despite a number of studies dedicated to the sensitivity of deep convection simulations to the properties of the rimed ice species in microphysics schemes, no consensus has been achieved on the nature of the impact. Considering the need for improved quantitative precipitation forecasts, it is crucial that the cloud modeling community better understands the reasons for these differing conclusions and knows the relevance of these sensitivities for the numerical weather prediction. This study examines the role of environmental conditions and storm type on the sensitivity of precipitation simulations to the nature of the rimed ice species (graupel or hail). Idealized 3D simulations of supercells/multicells and squall lines have been performed in varying thermodynamic environments. It has been shown that for simulation periods of sufficient length (>2 h), graupel-containing and hail-containing storms produce domain-averaged surface precipitation that is more similar than many earlier studies suggest. While graupel is lofted to higher altitudes and has a longer residence time aloft than hail, these simulations suggest that most of this graupel eventually reaches the surface and the surface precipitation rates of hail- and graupel-containing storms converge. However, environmental conditions play an important role in the magnitude of this sensitivity. Storms in large-CAPE environments (typical of storms in the U.S. Midwest) are more sensitive than their low-CAPE counterparts (typical of storms in Europe) to the nature of the rimed ice species in terms of domain-average surface precipitation. Supercells/multicells are more sensitive than squall lines to the nature of the rimed ice species in terms of spatial precipitation distribution and peak precipitation, disregarding of the amount of CAPE.

scholarly journals Evaluation of the COSMO model (v5.1) in polarimetric radar space – Impact of uncertainties in model microphysics, retrievals, and forward operator

2021 ◽  
Author(s):  
Prabhakar Shrestha ◽  
Jana Mendrok ◽  
Velibor Pejcic ◽  
Silke Trömel ◽  
Ulrich Blahak ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Model Space ◽  
Polarimetric Radar ◽  
Surface Precipitation ◽  
The Difference ◽  
Microphysical Processes ◽  
Nwp Model ◽  
Two Parameters ◽  
Ice Water ◽  
Forward Operator

Abstract. Sensitivity experiments with a numerical weather prediction (NWP) model and polarimetric radar forward operator (FO) are conducted for a long-duration stratiform event over northwestern Germany, to evaluate uncertainties in the partitioning of the ice water content and assumptions of hydrometeor scattering properties in the NWP model and FO, respectively. Polarimetric observations from X-band radar and retrievals of hydrometeor classifications are used for comparison with the multiple experiments in radar and model space. Modifying two parameters (Dice and Tgr) responsible for the production of snow and graupel, respectively, was found to improve the synthetic polarimetric moments and simulated hydrometeor population, while keeping the difference in surface precipitation statistically insignificant at model resolvable grid scales. However, the model still exhibited a low bias in simulated polarimetric moments at lower levels above the melting layer (−3 to −13 °C) where snow was found to dominate. This necessitates further research into the missing microphysical processes in these lower levels (e.g., fragmentation due to ice-ice collisions), and use of more reliable snow scattering models to draw valid conclusions.

scholarly journals Evaluation of the COSMO model (v5.1) in polarimetric radar space – impact of uncertainties in model microphysics, retrievals and forward operators

2022 ◽  
Vol 15 (1) ◽  
pp. 291-313
Author(s):  
Prabhakar Shrestha ◽  
Jana Mendrok ◽  
Velibor Pejcic ◽  
Silke Trömel ◽  
Ulrich Blahak ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Critical Diameter ◽  
Model Space ◽  
Threshold Temperature ◽  
Polarimetric Radar ◽  
Surface Precipitation ◽  
The Difference ◽  
Microphysical Processes ◽  
Nwp Model ◽  
Lower Magnitude

Abstract. Sensitivity experiments with a numerical weather prediction (NWP) model and polarimetric radar forward operator (FO) are conducted for a long-duration stratiform event over northwestern Germany to evaluate uncertainties in the partitioning of the ice water content and assumptions of hydrometeor scattering properties in the NWP model and FO, respectively. Polarimetric observations from X-band radar and retrievals of hydrometeor classifications are used for comparison with the multiple experiments in radar and model space. Modifying the critical diameter of particles for ice-to-snow conversion by aggregation (Dice) and the threshold temperature responsible for graupel production by riming (Tgr), was found to improve the synthetic polarimetric moments and simulated hydrometeor population, while keeping the difference in surface precipitation statistically insignificant at model resolvable grid scales. However, the model still exhibited a low bias (lower magnitude than observation) in simulated polarimetric moments at lower levels above the melting layer (−3 to −13 ∘C) where snow was found to dominate. This necessitates further research into the missing microphysical processes in these lower levels (e.g. fragmentation due to ice–ice collisions) and use of more reliable snow-scattering models to draw valid conclusions.

scholarly journals GEOVISUAL ANALYTICS ON THE VERIFICATION OF THE PAGASA OPERATIONAL NUMERICAL WEATHER PREDICTION MODEL RAINFALL FORECAST

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 215-222
Author(s):  
R. A. A. Flores
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Model Performance ◽  
Time Frame ◽  
Model Verification ◽  
Climate Type ◽  
Surface Precipitation ◽  
Numerical Weather ◽  
Geovisual Analytics ◽  
Nwp Model

Abstract. Assessment of NWP model performance is an integral part of operational forecasting as well as in research and development. Understanding the bias propagation of an NWP model and how it propagates across space can provide more insight in determining underlying causes and weaknesses not easily determined in traditional methods. The study aims to introduce the integration of the spatial distribution of error in interpreting model verification results by assessing how well the operational numerical weather prediction system of PAGASA captures the country’s weather pattern in each of its climate type. It also discusses improvements in model performance throughout the time-frame of analysis. Error propagation patterns were identified using Geovisual Analytics to allow comparison of verification scores among individual stations. The study concluded that a major update in the physics parameterization of the model in 2016 and continued minor updates in the following years, surface precipitation forecasts greatly improved from an average RMSE of 9.3, MAE of 3.2 and Bias of 1.36 in 2015 to an RMSE of 7.9, MAE of 2.5 and bias of −0.63 in 2018.

scholarly journals Combined use of volume radar observations and high-resolution numerical weather predictions to estimate precipitation at the ground: methodology and proof of concept

2019 ◽  
Vol 12 (10) ◽  
pp. 5669-5684 ◽  
Author(s):  
Tony Le Bastard ◽  
Olivier Caumont ◽  
Nicolas Gaussiat ◽  
Fatima Karbou
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Ground Level ◽  
Radar Data ◽  
Horizontal Resolution ◽  
Computationally Efficient ◽  
New Approach ◽  
Numerical Weather ◽  
Simulated Precipitation ◽  
Nwp Model

Abstract. The extrapolation of the precipitation to the ground from radar reflectivities measured at the beam altitude is one of the most delicate phases of radar data processing for producing quantitative precipitation estimations (QPEs) and remains a major scientific issue. In many operational meteorological services such as Météo-France, a vertical profile of reflectivity (VPR) correction is uniformly applied over a large part or the entire radar domain. This method is computationally efficient, and the overall bias induced by the bright band is most of the time well corrected. However, this way of proceeding is questionable in situations with high spatial and vertical variability of precipitation (during the passage of a cold front or in a complex terrain, for example). This study initiates from two statements: first, radars provide information on precipitation with a high spatio-temporal resolution but still require VPR corrections to extrapolate rain rates at the ground level. Second, the horizontal resolution of some numerical weather prediction (NWP) models is now comparable with the radar one, and their dynamical core and microphysics schemes allow the production of realistic simulations of VPRs. The present paper proposes a new approach to assess surface rainfall from radar reflectivity aloft by exploiting simulated VPRs and rainfall forecasts from the high-resolution NWP model AROME-NWC. To our knowledge, this is the first time that simulated precipitation profiles from an NWP model are used to derive radar QPEs. The implementation of the new method on two stratiform situations provided significant improvements on the hourly and 6 h accumulations compared to the operational QPEs, showing the relevance of this new approach.

scholarly journals On the Vertical Structure of Modeled and Observed Deep Convective Storms: Insights for Precipitation Retrieval and Microphysical Parameterization

2005 ◽  
Vol 44 (12) ◽  
pp. 1866-1884 ◽  
Author(s):  
Jamie L. Smedsmo ◽  
Efi Foufoula-Georgiou ◽  
Venugopal Vuruputur ◽  
Fanyou Kong ◽  
Kelvin Droegemeier
Keyword(s):  
High Resolution ◽  
Vertical Structure ◽  
Weather Prediction ◽  
Cloud Microphysics ◽  
Future Research ◽  
Surface Precipitation ◽  
Convective Storms ◽  
Precipitation Forecasting ◽  
Radar Echoes ◽  
Microphysical Parameterization

Abstract An understanding of the vertical structure of clouds is important for remote sensing of precipitation from space and for the parameterization of cloud microphysics in numerical weather prediction (NWP) models. The representation of cloud hydrometeor profiles in high-resolution NWP models has direct applications in inversion-type precipitation retrieval algorithms [e.g., the Goddard profiling (GPROF) algorithm used for retrieval of precipitation from passive microwave sensors] and in quantitative precipitation forecasting. This study seeks to understand how the vertical structure of hydrometeors (liquid and frozen water droplets in a cloud) produced by high-resolution NWP models with explicit microphysics, henceforth referred to as cloud-resolving models (CRMs), compares to observations. Although direct observations of 3D hydrometeor fields are not available, comparisons of modeled and observed radar echoes can provide some insight into the vertical structure of hydrometeors and, in turn, into the ability of CRMs to produce precipitation structures that are consistent with observations. Significant differences are revealed between the vertical structure of observed and modeled clouds of a severe midlatitude storm over Texas for which the surface precipitation is reasonably well captured. Possible reasons for this discrepancy are presented, and the need for future research is highlighted.

scholarly journals Combined use of volume radar observations and high-resolution numerical weather predictions to estimate precipitation at the ground: methodology and proof of concept

2019 ◽  
Author(s):  
Tony Le Bastard ◽  
Olivier Caumont ◽  
Nicolas Gaussiat ◽  
Fatima Karbou
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Ground Level ◽  
Radar Data ◽  
Horizontal Resolution ◽  
Computationally Efficient ◽  
New Approach ◽  
Numerical Weather ◽  
Simulated Precipitation ◽  
Nwp Model

Abstract. The extrapolation of the precipitation to the ground from radar reflectivities measured at the beam altitude is one of the most delicate phases of radar data processing for producing Quantitative Precipitation Estimations (QPEs) and remains a major scientific issue. In many operational meteorological services such as Météo-France, a Vertical Profile of Reflectivity (VPR) correction is uniformly applied over a large part or the entire radar domain. This method is computationally efficient and the overall bias induced by the bright band is most of the time well corrected. However, this way of proceeding is questionable in situations with high spatial and vertical variability of precipitation (during the passage of a cold front or in a complex terrain, for example). This study initiates from two statements: first, radars provide information on precipitation with a high spatio-temporal resolution but still require VPR corrections to extrapolate rain rates at the ground level. Second, the horizontal resolution of some Numerical Weather Prediction (NWP) models is now comparable with the radar one and their dynamical core and microphysics schemes allow to produce realistic simulations of VPRs. The present paper proposes a new approach to assess surface rainfall from radar reflectivity aloft by exploiting simulated VPRs and rainfall forecasts from the high resolution NWP model AROME-NWC. To our knowledge, this is the first time that simulated precipitation profiles from a NWP model are used to derive radar QPEs. The implementation of the new method on two stratiform situations provided significant improvements on the hourly and 6-h accumulations compared to the operational QPEs, showing the relevance of this new approach.

An Empirical Study of the Relationship between Seasonal Precipitation and Thermodynamic Environment in Puerto Rico

2019 ◽  
Vol 34 (2) ◽  
pp. 277-288 ◽  
Author(s):  
Paul W. Miller ◽  
Thomas L. Mote ◽  
Craig A. Ramseyer
Keyword(s):  
Puerto Rico ◽  
Annual Rainfall ◽  
Seasonal Precipitation ◽  
Surface Precipitation ◽  
Precipitation Forecasting ◽  
Caribbean Islands ◽  
K Index ◽  
Groundwater Reserves ◽  
The Tropics ◽  
The Relationship

Abstract With limited groundwater reserves and few reservoirs, Caribbean islands such as Puerto Rico are largely dependent on regular rainfall to meet societal and ecological water needs. Thus, the ability to anticipate seasonal rainfall shortages, such as the 2015 drought, is particularly important, yet few reliable tools exist for this purpose. Consequently, interpolated surface precipitation observations from the Daymet archive are summarized on daily, annual, and seasonal time scales and compared to the host thermodynamic environment as characterized by the Gálvez–Davison index (GDI), a convective potential parameter designed specifically for the tropics. Complementing the Daymet precipitation totals, ≥1.1 million WSR-88D volume scans between 2002 and 2016 were analyzed for echo tops ≥ 10 000 ft (~3 km) to establish a radar-inferred precipitation activity database for Puerto Rico. The 15-yr record reveals that the GDI outperforms several midlatitude-centric thermodynamic indices, explaining roughly 25% of daily 3-km echo top (ET) activity during each of Puerto Rico’s primary seasons. In contrast, neither mean-layer CAPE, the K index, nor total totals explain more than 11% during any season. When aggregated to the seasonal level, the GDI strongly relates to 3-km ET (R2 = 0.65) and Daymet precipitation totals (R2 = 0.82) during the early rainfall season (ERS; April–July), with correlations weaker outside of this period. The 4-month ERS explains 51% (41%) of the variability to Puerto Rico’s annual rainfall during exceptionally wet (dry) years. These findings are valuable for climate downscaling studies predicting Puerto Rico’s hydroclimate in future atmospheric states, and they could potentially be adapted for operational seasonal precipitation forecasting.

UTCI as the NWP model ALADIN (CHMI) output – first experiences

2021 ◽  
Vol 94 (2) ◽  
pp. 237-249
Author(s):  
Martin Novák
Keyword(s):  
Numerical Weather Prediction ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Climate Index ◽  
Basic Information ◽  
Universal Thermal Climate Index ◽  
Numerical Weather ◽  
Weather Forecasters ◽  
Thermal Climate ◽  
Nwp Model

The article includes a summary of basic information about the Universal Thermal Climate Index (UTCI) calculation by the numerical weather prediction (NWP) model ALADIN of the Czech Hydrometeorological Institute (CHMI). Examples of operational outputs for weather forecasters in the CHMI are shown in the first part of this work. The second part includes results of a comparison of computed UTCI values by ALADIN for selected place with UTCI values computed from real measured meteorological data from the same place.

scholarly journals Detection and thermal description of medicanes from numerical simulation

2014 ◽  
Vol 14 (5) ◽  
pp. 1059-1070 ◽  
Author(s):  
M. A. Picornell ◽  
J. Campins ◽  
A. Jansà
Keyword(s):  
Numerical Simulation ◽  
Thermal Structure ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Small Scale ◽  
Medium Range Weather Forecast ◽  
Warm Core ◽  
Strong Winds ◽  
Nwp Model ◽  
Ecmwf Model

Abstract. Tropical-like cyclones rarely affect the Mediterranean region but they can produce strong winds and heavy precipitations. These warm-core cyclones, called MEDICANES (MEDIterranean hurriCANES), are small in size, develop over the sea and are infrequent. For these reasons, the detection and forecast of medicanes are a difficult task and many efforts have been devoted to identify them. The goals of this work are to contribute to a proper description of these structures and to develop some criteria to identify medicanes from numerical weather prediction (NWP) model outputs. To do that, existing methodologies for detecting, characterizating and tracking cyclones have been adapted to small-scale intense cyclonic perturbations. First, a mesocyclone detection and tracking algorithm has been modified to select intense cyclones. Next, the parameters that define the Hart's cyclone phase diagram are tuned and calculated to examine their thermal structure. Four well-known medicane events have been described from numerical simulation outputs of the European Centre for Medium-Range Weather Forecast (ECMWF) model. The predicted cyclones and their evolution have been validated against available observational data and numerical analyses from the literature.

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