The Life-Cycle of Cloud and Precipitation Microphysics in Radar Observation and Numerical Model

The representation of microphysical processes in numerical weather prediction models remains a main source of uncertainty. To tackle this issue, we exploit the synergy of two polarimetric radars to provide novel observations of model microphysics parameterizations. In the framework of the IcePolCKa project (Investigation of the initiation of Convection and the Evolution of Precipitationusing simulatiOns and poLarimetric radar observations at C- and Ka-band) we use these observations to study the initiation of convection as well as the evolution of precipitation. At a distance of 23 km between the C-band PoldiRad radar of the German Aerospace Center (DLR) in Oberpfaffenhofen and the Ka-band Mira35 radar of the Ludwig-Maximilians-University of Munich (LMU), the two radar systems allow targeted observations and coordinated scan patterns. A second C-band radar located in Isen and operated by the German Weather Service (DWD) provides area coverage and larger spatial context. By tracking the precipitation movement, the dual-frequency and polarimetric radar observations allow us to characterize important microphysical parameters, such as predominant hydrometeor class or conversion rates between these classes over a significant fraction of the life time of a convective cell. A WRF (Weather Research and Forecasting Model) simulation setup has been established including a Europe-, a nested Germany- and a nested Munich- domain. The Munich domain covers the overlap area of our two radars Mira35 and Poldirad with a horizontal resolution of 400 m. For each of our measurement days we conduct a WRF hindcast simulation with differing microphysics schemes. To allow for a comparison between model world and observation space, we make use of the radar forward-simulator CR-SIM. The measurements so far include 240 coordinated scans of 36 different convective cells over 10 measurement days between end of April and mid July 2019 as well as 40 days of general dual-frequency volume scans between mid April and early October 2020. The performance of each microphysics scheme is analyzed through a comparison to our radar measurements on a statistical basis over all our measurements.

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From model to radar variables: a new forward polarimetric radar operator for COSMO

Atmospheric Measurement Techniques ◽

10.5194/amt-11-3883-2018 ◽

2018 ◽

Vol 11 (7) ◽

pp. 3883-3916 ◽

Cited By ~ 6

Author(s):

Daniel Wolfensberger ◽

Alexis Berne

Keyword(s):

Phase Shift ◽

Solid Phase ◽

Weather Prediction ◽

Doppler Spectrum ◽

Polarimetric Radar ◽

Dual Frequency ◽

Differential Phase Shift ◽

Radar Observations ◽

Differential Phase ◽

Differential Reflectivity

Abstract. In this work, a new forward polarimetric radar operator for the COSMO numerical weather prediction (NWP) model is proposed. This operator is able to simulate measurements of radar reflectivity at horizontal polarization, differential reflectivity as well as specific differential phase shift and Doppler variables for ground based or spaceborne radar scans from atmospheric conditions simulated by COSMO. The operator includes a new Doppler scheme, which allows estimation of the full Doppler spectrum, as well a melting scheme which allows representing the very specific polarimetric signature of melting hydrometeors. In addition, the operator is adapted to both the operational one-moment microphysical scheme of COSMO and its more advanced two-moment scheme. The parameters of the relationships between the microphysical and scattering properties of the various hydrometeors are derived either from the literature or, in the case of graupel and aggregates, from observations collected in Switzerland. The operator is evaluated by comparing the simulated fields of radar observables with observations from the Swiss operational radar network, from a high resolution X-band research radar and from the dual-frequency precipitation radar of the Global Precipitation Measurement satellite (GPM-DPR). This evaluation shows that the operator is able to simulate an accurate Doppler spectrum and accurate radial velocities as well as realistic distributions of polarimetric variables in the liquid phase. In the solid phase, the simulated reflectivities agree relatively well with radar observations, but the simulated differential reflectivity and specific differential phase shift upon propagation tend to be underestimated. This radar operator makes it possible to compare directly radar observations from various sources with COSMO simulations and as such is a valuable tool to evaluate and test the microphysical parameterizations of the model.

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From model to radar variables: a new forward polarimetric radar operator for COSMO

10.5194/amt-2017-427 ◽

2017 ◽

Author(s):

Daniel Wolfensberger ◽

Alexis Berne

Keyword(s):

Phase Shift ◽

Solid Phase ◽

Weather Prediction ◽

Doppler Spectrum ◽

Polarimetric Radar ◽

Dual Frequency ◽

Differential Phase Shift ◽

Radar Observations ◽

Differential Phase ◽

Differential Reflectivity

Abstract. In this work, a new forward polarimetric radar operator for the COSMO numerical weather prediction (NWP) model is proposed. This operator is able to simulate measurements of radar reflectivity at horizontal polarization, differential reflectivity as well as specific differential phase shift and Doppler variables for ground based or spaceborne radar scans from atmospheric conditions simulated by COSMO. The operator includes a new Doppler scheme, which allows to estimate the full Doppler spectrum, as well a melting scheme which allows to represent the very specific polarimetric signature of melting hydrometeors. In addition, the operator is adapted to both the operational one-moment microphysical scheme of COSMO and its more advanced two-moment scheme. The parameters of the relationships between the microphysical and scattering properties of the various hydrometeors are derived either from the literature or, in the case of graupel and aggregates, from observations collected in Switzerland. The operator is evaluated by comparing the simulated fields of radar observables with observations from the Swiss operational radar network, from a high resolution X-band research radar and from the dual-frequency precipitation radar of the Global Precipitation Measurement satellite (GPM-DPR). This evaluation shows that the operator is able to simulate an accurate Doppler spectrum and accurate radial velocities as well as realistic distributions of polarimetric variables in the liquid phase. In the solid phase, the simulated reflectivities agree relatively well with radar observations, but the simulated differential reflectivity and specific differential phase shift upon propagation tend to be underestimated. This radar operator makes it possible to compare directly radar observations from various sources with COSMO simulations and as such is a valuable tool to evaluate and test the microphysical parameterizations of the model.

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A Robust Dual-Frequency Radar Profiling Algorithm

Journal of Applied Meteorology and Climatology ◽

10.1175/2011jamc2655.1 ◽

2011 ◽

Vol 50 (7) ◽

pp. 1543-1557 ◽

Cited By ~ 25

Author(s):

Mircea Grecu ◽

Lin Tian ◽

William S. Olson ◽

Simone Tanelli

Keyword(s):

Synthetic Data ◽

Optimization Procedure ◽

Correction Method ◽

Single Frequency ◽

Retrieval Algorithm ◽

Dual Frequency ◽

Ka Band ◽

Radar Observations ◽

Distribution Shape ◽

Ku Band

AbstractIn this study, an algorithm to retrieve precipitation from spaceborne dual-frequency (13.8 and 35.6 GHz, or Ku/Ka band) radar observations is formulated and investigated. Such algorithms will be of paramount importance in deriving radar-based and combined radar–radiometer precipitation estimates from observations provided by the forthcoming NASA Global Precipitation Measurement (GPM) mission. In GPM, dual-frequency Ku-/Ka-band radar observations will be available only within a narrow swath (approximately one-half of the width of the Ku-band radar swath) over the earth’s surface. Therefore, a particular challenge is to develop a flexible radar retrieval algorithm that can be used to derive physically consistent precipitation profile estimates across the radar swath irrespective of the availability of Ka-band radar observations at any specific location inside that swath, in other words, an algorithm capable of exploiting the information provided by dual-frequency measurements but robust in the absence of Ka-band channel. In the present study, a unified, robust precipitation retrieval algorithm able to interpret either Ku-only or dual-frequency Ku-/Ka-band radar observations in a manner consistent with the information content of the observations is formulated. The formulation is based on 1) a generalized Hitschfeld–Bordan attenuation correction method that yields generic Ku-only precipitation profile estimates and 2) an optimization procedure that adjusts the Ku-band estimates to be physically consistent with coincident Ka-band reflectivity observations and surface reference technique–based path-integrated attenuation estimates at both Ku and Ka bands. The algorithm is investigated using synthetic and actual airborne radar observations collected in the NASA Tropical Composition, Cloud, and Climate Coupling (TC4) campaign. In the synthetic data investigation, the dual-frequency algorithm performed significantly better than a single-frequency algorithm; dual-frequency estimates, however, are still sensitive to various assumptions such as the particle size distribution shape, vertical and cloud water distributions, and scattering properties of the ice-phase precipitation.

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Improvements to melting snow behavior in an NWP bulk microphysics scheme

10.5194/egusphere-egu2020-21924 ◽

2020 ◽

Author(s):

Emilie C. Iversen ◽

Gregory Thompson ◽

Bjørn Egil Nygaard

Keyword(s):

Prediction Models ◽

Weather Prediction ◽

Wrf Model ◽

Bimodal Distribution ◽

Fall Velocity ◽

Microphysics Scheme ◽

Melting Snow ◽

Melting Layer ◽

Computational Performance ◽

Fall Speed

Snow falling into a melting layer will eventually consist of a fraction of meltwater and hence change its characteristics in terms of size, shape, density, fall speed and stickiness. Given that these characteristics contribute to determine the phase and amount of precipitation reaching the ground, precisely predicting such are important in order to obtain accurate weather forecasts for which society depends on. For example, in hydrological modelling precipitation phase at the surface is a first-order driver of hydrological processes in a water shed. Also, melting snow exerts a possible threat to critical infrastructure because the wet, sticky snow may adhere to the structures and form heavy ice sleeves.Most widely used bulk microphysical parameterization schemes part of numerical weather prediction models represent only purely solid or liquid hydrometeors, and so melting particle characteristics are either ignored or represented by parent species with simple conditions for behavior in the melting layer. The Thompson microphysics scheme is explicitly developed for forecasting winter conditions in real-time as part of the WRF model, and to maintain computational performance, the introduction of additional prognostic variables is undesirable. This research aims at improving the Thompson scheme with respect to melting snow characteristics using a physically based approximation for the snowflake melted fraction, as well as a new definition of melting level and melting particle fall velocity. A real 3D WRF case is set up to compare with in-situ measurements of hydrometeor size and fall velocity from a disdrometer and a vertically pointing Doppler radar deployed during the Olympic Mountain Experiment (OLYMPEX). The modified microphysics scheme is able to replicate the bimodal distribution of fall speed &#8211; diameter relations typical of mixed precipitation seen in disdrometer data, as well as the non-linear increase in snow fall speed with melted fraction through the melting layer.

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Coupled Stratospheric Chemistry-Meteorology Data Assimilation. Part I: Modeling Chemistry-Dynamics Interactions

10.20944/preprints201910.0233.v1 ◽

2019 ◽

Author(s):

Richard Ménard ◽

Simon Chabrillat ◽

Alain Robichaud ◽

Jean de Grandpré ◽

Martin Charron ◽

...

Keyword(s):

Atmospheric Chemistry ◽

Transport Model ◽

Model Simulation ◽

Weather Prediction ◽

Coupled Model ◽

Horizontal Resolution ◽

Chemical Transport Model ◽

Stratospheric Chemistry ◽

Temperature Bias ◽

Meteorological Analysis

A coupled stratospheric chemistry-meteorology model was developed by combining the Canadian operational weather prediction model Global Environmental Multiscale (GEM) with a comprehensive stratospheric photochemistry model from the Belgian Assimilation System for Chemical ObsErvations (BASCOE). The coupled model was called GEM-BACH for GEM-Belgian Atmospheric CHemistry. The coupling was made across a chemical interface that preserves time splitting while being modular, allowing GEM to run with or without chemistry. An evaluation of the coupling was performed by comparing the coupled model, refreshed by meteorological analyses every 6 hours, against the standard offline chemical transport model (CTM) approach. Results show that the dynamical meteorological consistency between meteorological analysis times far outweighs the error created by the jump resulting from the meteorological analysis increments at regular time intervals, irrespective whether a 3D-Var or 4D-Var meteorological analysis is used. GEM-BACH forecast refreshed by meteorological analyses every 6 hours were compared against independent measurements of temperature, long-lived species, ozone and water vapor. The comparison showed a relatively good agreement throughout the stratosphere except for an upper-level warm temperature bias and an ozone deficit of nearly 15%. Arguments in favor of using the same horizontal resolution for chemistry, meteorology, and meteorological analysis increments are also presented. In particular, the coupled model simulation during an ozone hole event gives better ozone concentrations than a 4D-Var chemical assimilation at a lower resolution.

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Overview: Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes

Atmospheric Chemistry and Physics ◽

10.5194/acp-21-17291-2021 ◽

2021 ◽

Vol 21 (23) ◽

pp. 17291-17314

Author(s):

Silke Trömel ◽

Clemens Simmer ◽

Ulrich Blahak ◽

Armin Blanke ◽

Sabine Doktorowski ◽

...

Keyword(s):

Weather Prediction ◽

Polarimetric Radar ◽

Atmospheric Models ◽

German Research Foundation ◽

Radar Observations ◽

Atmospheric Modelling ◽

Radar Polarimetry ◽

Radar Network ◽

Precipitation Processes ◽

National Networks

Abstract. Cloud and precipitation processes are still a main source of uncertainties in numerical weather prediction and climate change projections. The Priority Programme “Polarimetric Radar Observations meet Atmospheric Modelling (PROM)”, funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), is guided by the hypothesis that many uncertainties relate to the lack of observations suitable to challenge the representation of cloud and precipitation processes in atmospheric models. Such observations can, however, at present be provided by the recently installed dual-polarization C-band weather radar network of the German national meteorological service in synergy with cloud radars and other instruments at German supersites and similar national networks increasingly available worldwide. While polarimetric radars potentially provide valuable in-cloud information on hydrometeor type, quantity, and microphysical cloud and precipitation processes, and atmospheric models employ increasingly complex microphysical modules, considerable knowledge gaps still exist in the interpretation of the observations and in the optimal microphysics model process formulations. PROM is a coordinated interdisciplinary effort to increase the use of polarimetric radar observations in data assimilation, which requires a thorough evaluation and improvement of parameterizations of moist processes in atmospheric models. As an overview article of the inter-journal special issue “Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes”, this article outlines the knowledge achieved in PROM during the past 2 years and gives perspectives for the next 4 years.

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Intense sea-effect snowfall case on the western coast of Finland

Advances in Science and Research ◽

10.5194/asr-14-231-2017 ◽

2017 ◽

Vol 14 ◽

pp. 231-239 ◽

Cited By ~ 3

Author(s):

Taru Olsson ◽

Tuuli Perttula ◽

Kirsti Jylhä ◽

Anna Luomaranta

Keyword(s):

Preliminary Analysis ◽

Temporal Evolution ◽

Model Simulation ◽

Weather Prediction ◽

Convective Available Potential Energy ◽

Horizontal Resolution ◽

Convective Precipitation ◽

Western Coast ◽

Radar Images ◽

A Value

Abstract. A new national daily snowfall record was measured in Finland on 8 January 2016 when it snowed 73 cm (31 mm as liquid water) in less than a day in Merikarvia on the western coast of Finland. The area of the most intense snowfall was very small, which is common in convective precipitation. In this work we used hourly weather radar images to identify the sea-effect snowfall case and to qualitatively estimate the performance of HARMONIE, a non-hydrostatic convection-permitting weather prediction model, in simulating the spatial and temporal evolution of the snowbands. The model simulation, including data assimilation, was run at 2.5 km horizontal resolution and 65 levels in vertical. HARMONIE was found to capture the overall sea-effect snowfall situation quite well, as both the timing and the location of the most intense snowstorm were properly simulated. Based on our preliminary analysis, the snowband case was triggered by atmospheric instability above the mostly ice-free sea and a low-level convergence zone almost perpendicular to the coastline. The simulated convective available potential energy (CAPE) reached a value of 87 J kg−1 near the site of the observed snowfall record.

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

Weather and Forecasting ◽

10.1175/waf-d-21-0104.1 ◽

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.

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Postprocessing wind forecasts with deep learning in complex terrain

10.5194/egusphere-egu21-9849 ◽

2021 ◽

Author(s):

Daniele Nerini ◽

Jonas Bhend ◽

Christoph Spirig ◽

Lionel Moret ◽

Mark Liniger

Keyword(s):

Prediction Models ◽

Weather Prediction ◽

Arbitrary Point ◽

Horizontal Resolution ◽

Supervised Machine Learning ◽

Regression Problem ◽

Numerical Predictions ◽

Operational Forecasts ◽

First Results ◽

Elevation Model

To improve and automate the quality of weather forecasts to the public, MeteoSwiss is redesigning its statistical postprocessing suite. The effort aims at producing calibrated probabilistic predictions to any arbitrary point in space and up to a 15-day lead time, by seamlessly integrating multiple numerical weather prediction models into a unique consensus forecast.For hourly wind forecasts (mean, gust, and direction), the task is formulated as a regression problem in a supervised machine learning framework, where station measurements are used as labels, and co-located NWP forecasts as features. To improve the estimates at ungauged locations, additional static topographical features are derived from a 50m digital elevation model. The probabilistic component is included by training the neural network not to produce a deterministic prediction, but the parameters of a conditional probability function. To this end, the Continuous Ranked Probability Score (CRPS) is used as a loss function.The dataset includes a range of surface parameters at hourly resolution produced by the operational forecasts from three NWP models (the deterministic COSMO-1 model, at 1 km horizontal resolution; the 21-member COSMO-E, 2 km; and the 51-member ECMWF IFS ENS at about 18 km). The data cover the whole of Switzerland over a period spanning more than four years (mid 2016 to end of 2020). Wind measurements from over 500 surface weather stations are included as reference dataset. The study uses a train-validation-test split in both space and time to assess the ability of the postprocessing model to generalize to unseen locations and times.The results indicate that, despite the challenging nature of the problem, the postprocessing model can improve over the baseline NWP forecasts in terms of CRPS on the test set. In particular, the model is effectively correcting for biases relating to altitude error and other misrepresentations in the NWP topography. The results show that it is feasible to downscale numerical predictions to a substantially higher spatial resolution. Moreover, the conditional probabilities shows consistent improvements in terms of calibration, although it remains a significant portions of undetected peak events (positive outliers), possibly to be related to unpredictable phenomena (e.g., thunderstorm gusts). Finally, first results seem to suggest that the gain in prediction skill is mainly driven by a better statistical reliability rather than higher statistical resolution.

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Convection is key to better polar low forecasts

10.5194/egusphere-egu2020-10163 ◽

2020 ◽

Author(s):

Matilda Hallerstig ◽

Linus Magnusson ◽

Erik Kolstad

Keyword(s):

Prediction Models ◽

Deterministic Model ◽

Rapid Development ◽

Weather Prediction ◽

Model Performance ◽

Horizontal Resolution ◽

Added Value ◽

Grid Spacing ◽

Polar Lows ◽

Polar Low

ECMWF HRES and Arome Arctic are the operational Numerical Weather Prediction models that forecasters in northern Norway use to predict Polar lows in the Nordic and Barents Seas. These type of lows are small, but intense mesoscale cyclones with strong, gusty winds and heavy snow showers. They cause hazards like icing, turbulence, high waves and avalanches that threaten offshore activity and coastal societies in the area. Due to their small size and rapid development, medium range global models with coarser resolutions such as ECMWF have not been able to represent them properly. This was only possible with short range high resolution regional models like Arome. When ECMWF introduced their new HRES deterministic model with 9 km grid spacing, the potential for more precise polar low forecasts increased. Here we use case studies and sensitivity tests to examine the ability of ECMWF HRES to represent polar lows. We also evaluate what added value the Arome Arctic model with 2.5 km grid spacing gives. For verification, we use coastal meteorological stations and scatterometer winds. We found that convection has a greater impact on model performance than horizontal resolution. We also see that Arome Arctic produces higher wind speeds than ECMWF HRES. To improve performance during polar lows for models with a horizontal grid spacing less than 10 km, it is therefore more important to improve the understanding and formulation of convective processes rather than simply increasing horizontal resolution.

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