Severe Weather Case Research Articles

Exploring Ensemble Forecast Sensitivity to Observations for a Convective-Scale Data Assimilation System over the Dallas–Fort Worth Testbed

Abstract Forecast sensitivity to observation (FSO) methods have become increasingly popular over the past two decades, providing the ability to quantify the impacts of various observing systems on forecasts without having to conduct costly data denial experiments. While adjoint- and ensemble-based FSO are employed in many global operational systems, their use for regional convection-allowing data assimilation (DA) and forecast systems have not been fully examined. In this study, ensemble FSO (EFSO) is explored for high-frequency convective-scale DA for a severe weather case study over the Dallas–Fort Worth testbed. This testbed, originally established by the Collaborative Adaptive Sensing of the Atmosphere (CASA) project, aims to improve high-resolution DA systems by assimilating a variety of existing state and regional mesoscale observing systems to fill gaps of conventional observing networks. This study utilizes EFSO to estimate relative impacts of nonconventional surface observations against conventional observations, and further incorporates assimilated radar observations into EFSO. Results show that, when applying advected localization and a neighborhood upscale averaging technique, EFSO estimates remain correlated and skillful with the actual error reduction of all assimilated observations for the duration of 2-h forecasts. The ability for EFSO to verify against other metrics (surface T, u, υ, q) beside energy norms is also demonstrated, emphasizing that EFSO can be used to evaluate impacts of specific parts of the forecast system rather than integrated quantities. Partitioned EFSO revealed that while conventional and radar observations contributed to most of the total energy, nonconventional observations contributed a significant percentage (up to 25%) of the total impact to surface thermodynamic fields.

The Impact of Assimilating Satellite-Derived Layered Precipitable Water, Cloud Water Path, and Radar Data on Short-Range Thunderstorm Forecasts

AbstractWith the launch of GOES-16 in November 2016, effective utilization of its data in convective-scale numerical weather prediction (NWP) has the potential to improve high-impact weather (HIWeather) forecasts. In this study, the impact of satellite-derived layered precipitable water (LPW) and cloud water path (CWP) in addition to NEXRAD observations on short-term convective-scale NWP forecasts are examined using three severe weather cases that occurred in May 2017. In each case, satellite-derived CWP and LPW products and radar observations are assimilated into the Advanced Research Weather Research and Forecasting (WRF-ARW) Model using the NSSL hybrid Warn-on-Forecast (WoF) analysis and forecast system. The system includes two components: the GSI-EnKF system and a deterministic 3DEnVAR system. This study examines deterministic 0–6-h forecasts launched from the hybrid 3DEnVAR analyses for the three severe weather events. Three types of experiments are conducted and compared: (i) the control experiment (CTRL) without assimilating any data, (ii) the radar experiment (RAD) with the assimilation of radar and surface observations, and (iii) the satellite experiment (RADSAT) with the assimilation of all observations including surface-, radar-, and satellite-derived CWP and LPW. The results show that assimilating additional GOES products improves short-range forecasts by providing more accurate initial conditions, especially for moisture and temperature variables.

Numerical simulations of June 7, 2020 convective precipitation over Slovakia using deterministic, probabilistic, and convection-permitting approaches

The paper presented is dedicated to the evaluation of the influence of various improvements to the numerical weather prediction (NWP) systems exploited at the Slovak Hydrometeorological Institute (SHMÚ). The impact was illustrated in a case study with multicell thunderstorms and the results were confronted with the reference analyses from the INCA nowcasting system, regional radar reflectivity data, and METEOSAT satellite imagery. The convective cells evolution was diagnosed in non-hydrostatic dynamics experiments to study weak mesoscale vortices and updrafts. The growth of simulated clouds and evolution of the temperature at their top were compared with the brightness temperature analyzed from satellite imagery. The results obtained indicated the potential for modeling and diagnostics of small-scale structures within the convective cloudiness, which could be related to severe weather. Furthermore, the non-hydrostatic dynamics experiments related to the stability and performance improvement of the time scheme led to the formulation of a new approach to linear operator definition for semi-implicit scheme (in text referred as NHHY). We demonstrate that the execution efficiency has improved by more than 20%. The exploitation of several high resolution measurement types in data assimilation contributed to more precise position of predicted patterns and precipitation representation in the case study. The non-hydrostatic dynamics provided more detailed structures. On the other hand, the potential of a single deterministic forecast of prefrontal heavy precipitation was not as high as provided by the ensemble system. The prediction of a regional ensemble system A-LAEF (ALARO Limited Area Ensemble Forecast) enhanced the localization of precipitation patterns. Though, this was rather due to the simulation of uncertainty in the initial conditions and also because of the stochastic perturbation of physics tendencies. The various physical parameterization setups of A-LAEF members did not exhibit a systematic effect on precipitation forecast in the evaluated case. Moreover, the ensemble system allowed an estimation of uncertainty in a rapidly developing severe weather case, which was high even at very short range.

Novel Thunderstorm Alert System (NOTHAS)

A “Novel Thunderstorm Alert System” (NOTHAS) has been developed and extensively tested for forecast and warnings of mid-latitude and tropical convective events. The design of the system showed some potential advantages compared to earlier alert systems, mainly in reducing uncertainties in predictions by taking the given maximum hourly local-scale signal. It represents a dynamic tool which allows the use of the probability concept of multivariate distribution and integrating it into general function by taking all convective parameters. It utilizes the latest developed microphysical parameterization scheme using a scale and aerosol awareness convective scheme and the sharpest criteria for mid-latitude storms. NOTHAS shows consistency and some kind of flexibility in post-processing applications, regardless of different parameterizations used in the ensemble or deterministic forecasts. The scientific verification shows a high level of accuracy in all significant scores which indicates that severe weather outlooks produced by NOTHAS for several hours ahead are in good alignment with observed thunderstorm activity. This novel tool shows a good performance which has sufficient merit for further additional testing and system evaluation of different severe mid-latitude and tropical storms, tropical cyclones and other severe weather cases across regions.

A methodology to conduct wind damage field surveys for high-impact weather events of convective origin

Abstract. Post-event damage assessments are of paramount importance to document the effects of high-impact weather-related events such as floods or strong wind events. Moreover, evaluating the damage and characterizing its extent and intensity can be essential for further analysis such as completing a diagnostic meteorological case study. This paper presents a methodology to perform field surveys of damage caused by strong winds of convective origin (i.e. tornado, downburst and straight-line winds). It is based on previous studies and also on 136 field studies performed by the authors in Spain between 2004 and 2018. The methodology includes the collection of pictures and records of damage to human-made structures and on vegetation during the in situ visit to the affected area, as well as of available automatic weather station data, witness reports and images of the phenomenon, such as funnel cloud pictures, taken by casual observers. To synthesize the gathered data, three final deliverables are proposed: (i) a standardized text report of the analysed event, (ii) a table consisting of detailed geolocated information about each damage point and other relevant data and (iii) a map or a KML (Keyhole Markup Language) file containing the previous information ready for graphical display and further analysis. This methodology has been applied by the authors in the past, sometimes only a few hours after the event occurrence and, on many occasions, when the type of convective phenomenon was uncertain. In those uncertain cases, the information resulting from this methodology contributed effectively to discern the phenomenon type thanks to the damage pattern analysis, particularly if no witness reports were available. The application of methodologies such as the one presented here is necessary in order to build homogeneous and robust databases of severe weather cases and high-impact weather events.

Weather Radar and Rain-Gauge Data Fusion for Quantitative Precipitation Estimation: Two Case Studies

In recent years, severe weather phenomena have occurred with increasing frequency throughout the Mediterranean area. Because of the extreme intensity of the phenomena and of their small spatio-temporal scales, an early warning of severe rainfall through a timely and accurate estimation is crucial for reducing the hydrological risk and for disaster mitigation. On the other hand, the rain-gauge networks are often not able to detect rainfall due to their limited sampling capability, as occurred in the two case studies presented in this work, characterized by severe weather conditions. For both case studies, we utilized a data-fusion procedure aimed at real-time estimation of cumulative rainfall fields, based on the reflectivity factor Z provided by ground weather radar and rain-gauge estimates of the rainfall intensity R. The use of Z-R relationships determined a priori, as typically done by operational weather services, is not appropriate for obtaining accurate rainfall estimates, as needed, for instance, to forecast flash flood events. Consequently, additional information is needed. The procedure utilized is based on an adaptive data-fusion technique, relying on the dynamic adjustment of the coefficients of the Z-R relationship to the observed phenomenon. Its application to two severe weather case studies demonstrated the capability of the methodology to correctly identify and monitor areas of high potential risk as well as to provide rainfall estimates in such areas.

Variational Assimilation of Radar Data and GLM Lightning-Derived Water Vapor for the Short-Term Forecasts of High-Impact Convective Events

Abstract The assimilation of water vapor mass mixing ratio derived from total lightning data from the Geostationary Lightning Mapper (GLM) within a three-dimensional variational (3DVAR) system is evaluated for the analysis and short-term forecast (≤6 h) of a high-impact convective event over the northern Great Plains in the United States. Building on recent work, the lightning data assimilation (LDA) method adjusts water vapor mass mixing ratio within a fixed layer depth above the lifted condensation level by assuming nearly water-saturated conditions at observed lightning locations. In this algorithm, the total water vapor mass added by the LDA is balanced by an equal removal outside observed lightning locations. Additional refinements were also devised to partially alleviate the seasonal and geographical dependence of the original scheme. To gauge the added value of lightning, radar data (radial velocity and reflectivity) were also assimilated with or without lightning. Although the method was evaluated in quasi–real time for several high-impact weather events throughout 2018, this work will focus on one specific, illustrative severe weather case wherein the control simulation—which did not assimilate any data—was eventually able to initiate and forecast the majority of the observed storms. Given a relatively reasonable forecast in the control experiment, the GLM and radar assimilation experiments were still able to improve the short-term forecast of accumulated rainfall and composite radar reflectivity further, as measured by neighborhood-based metrics. These results held whether the simulations made use of one single 3DVAR analysis or high-frequency (10 min) successive cycling over a 1-h period.

The impact of future urban scenarios on a severe weather case in the metropolitan area of São Paulo

In this work, convective parameters are applied, based on numerical simulations made with Brazilian Regional Atmospheric Modeling System (BRAMS) model, to a severe weather case which occurred in the metropolitan area of Sao Paulo (MASP). Scenarios of future urban area growth and increase of building heights were made to evaluate changes in convective parameters and rainfall for the study region. Using factorial planning and factor separation methods, we found that the urban area growth predicted for 2030 is capable of increasing the amount of precipitation, mainly due to the land use change from rural to urban. In the scenario of building heights increasing, it was found a tendency for rainfall suppression. The urban area for 2030 is the major factor contributing to increasing atmospheric instability and wind shear. Vertical urban growth causes an increase in atmospheric instability and a decrease in wind shear. The interaction between urban area and building height factors increases the amount of precipitation and storm motion over the MASP.

4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF

Abstract. The GNSS data assimilation is currently widely discussed in the literature with respect to the various applications for meteorology and numerical weather models. Data assimilation combines atmospheric measurements with knowledge of atmospheric behavior as codified in computer models. With this approach, the “best” estimate of current conditions consistent with both information sources is produced. Some approaches also allow assimilating the non-prognostic variables, including remote sensing data from radar or GNSS (global navigation satellite system). These techniques are named variational data assimilation schemes and are based on a minimization of the cost function, which contains the differences between the model state (background) and the observations. The variational assimilation is the first choice for data assimilation in the weather forecast centers, however, current research is consequently looking into use of an iterative, filtering approach such as an extended Kalman filter (EKF). This paper shows the results of assimilation of the GNSS data into numerical weather prediction (NWP) model WRF (Weather Research and Forecasting). The WRF model offers two different variational approaches: 3DVAR and 4DVAR, both available through the WRF data assimilation (WRFDA) package. The WRFDA assimilation procedure was modified to correct for bias and observation errors. We assimilated the zenith total delay (ZTD), precipitable water (PW), radiosonde (RS) and surface synoptic observations (SYNOP) using a 4DVAR assimilation scheme. Three experiments have been performed: (1) assimilation of PW and ZTD for May and June 2013, (2) assimilation of PW alone; PW, with RS and SYNOP; ZTD alone; and finally ZTD, with RS and SYNOP for 5–23 May 2013, and (3) assimilation of PW or ZTD during severe weather events in June 2013. Once the initial conditions were established, the forecast was run for 24 h. The major conclusion of this study is that for all analyzed cases, there are two parameters significantly changed once GNSS data are assimilated in the WRF model using GPSPW operator and these are moisture fields and rain. The GNSS observations improves forecast in the first 24 h, with the strongest impact starting from a 9 h lead time. The relative humidity forecast in a vertical profile after assimilation of ZTD shows an over 20 % decrease of mean error starting from 2.5 km upward. Assimilation of PW alone does not bring such a spectacular improvement. However, combination of PW, SYNOP and radiosonde improves distribution of humidity in the vertical profile by maximum of 12 %. In the three analyzed severe weather cases PW always improved the rain forecast and ZTD always reduced the humidity field bias. Binary rain analysis shows that GNSS parameters have significant impact on the rain forecast in the class above 1 mm h−1.

Comparison of Cloud Microphysics Schemes in a Warn-on-Forecast System Using Synthetic Satellite Objects

AbstractForecasts of high-impact weather conditions using convection-allowing numerical weather prediction models have been found to be highly sensitive to the selection of cloud microphysics scheme used within the system. The Warn-on-Forecast (WoF) project has developed a rapid-cycling, convection-allowing, data assimilation and forecasting system known as the NSSL Experimental WoF System for ensembles (NEWS-e), which is designed to utilize advanced cloud microphysics schemes. NEWS-e currently (2017–18) uses the double-moment NSSL variable density scheme (NVD), which has been shown to generate realistic representations of convective precipitation within the system. However, very little verification on nonprecipitating cloud features has been performed with this system. During the 2017 Hazardous Weather Testbed (HWT) experiment, an overestimation of the areal coverage of convectively generated cirrus clouds was observed. Changing the cloud microphysics scheme to Thompson generated more accurate cloud fields. This research undertook the task of improving the cloud analysis generated by NVD while maintaining its skill for other variables such as reflectivity. Adjustments to cloud condensation nuclei (CCN), fall speed, and collection efficiencies were made and tested over a set of six severe weather cases occurring during May 2017. This research uses an object-based verification approach in which objects of cold infrared brightness temperatures, high cloud-top pressures, and cloud water path are generated from model output and compared against GOES-13 observations. Results show that the modified NVD scheme generated much more skillful forecasts of cloud objects than the original formulation without having a negative impact on the skill of simulated composite reflectivity forecasts.

Modified NAM Microphysics for Forecasts of Deep Convective Storms

Abstract The Ferrier–Aligo (FA) microphysics scheme has been running operationally in the National Centers for Environmental Prediction (NCEP) North American Mesoscale Forecast System (NAM) since August 2014. It was developed to improve forecasts of deep convection in the NAM contiguous United States (CONUS) nest, and it replaces previous versions of the NAM microphysics. The FA scheme is the culmination of extensive microphysical scheme sensitivity experiments made over nearly a dozen warm- and cool-season severe weather cases, as well as an extensive real-time testing in a full, system-wide developmental version of the NAM. While the FA scheme advects each hydrometeor species separately, it was the mass-weighted rime factor (RF) that allowed rimed ice to be advected to very cold temperatures aloft and improved the vertical structure of deep convection. Rimed ice fall speeds were reduced in order to offset an increase in bias of heavy precipitation as a consequence of the mass-weighted RF advection. The FA scheme also incorporated findings from 3-km model runs using the Thompson scheme, including 1) improved closure assumptions for large precipitating ice that targeted the convective and anvil regions of storms, 2) a new diagnostic calculation of radar reflectivity from rimed ice in association with intense convection, and 3) a variable rain intercept parameter that reduced widespread spurious weak reflectivity from shallow boundary layer clouds and increased stratiform rainfall.

A radar-based centroid tracking algorithm for severe weather surveillance: identifying split/merge processes in convective systems

Nowcasting tools using radar data are essential when researching severe weather phenomena. Identifying thunderstorms and characterising their properties are usually the main steps to follow in order to make a good short-term forecast. These analyses are normally used to feed early warning systems in many countries, helping to reduce the impact of severe weather. Over the last few decades, research in this meteorological field has increased significantly, defining different methodologies that have worked well for many severe events. Most of these methodologies are based on a prior 3D identification of the thunderstorms in question, by applying intensity and size thresholds and obtaining their characteristics, allowing their centroids to be tracked. In some cases, the thresholds for identification and the distance established to track the centroid are too restrictive, which results in the incorrect identification of individual cells in multicell systems, or even means that the algorithm is unable to distinguish when an anomalous movement takes place. This paper presents a new type of 3D cell identification and tracking, based on the algorithm developed by Rigo and Llasat (2004). This last algorithm is currently used in the Meteorological Service of Catalonia (SMC), and consists of a nowcasting methodology based on centroid tracking. In the new version, a new two-step methodology to construct 3D radar cells has been proposed. A double reflectivity thresholding method plus an overlapping technique have been applied for a better definition of the convective structures. This makes it possible to identify possible changing processes such as splitting or merging within the same thunderstorm. Furthermore, a dynamic distance threshold technique has been adopted to correctly track cells with anomalous movements. An in-depth study has been carried out for a severe weather case in which a multicell system presented a complex merging and splitting process that was correctly forecasted using the proposed algorithm.

Weather Radar Spatiotemporal Saliency: A First Look at an Information Theory–Based Human Attention Model Adapted to Reflectivity Images

AbstractForecasters often monitor and analyze large amounts of data, especially during severe weather events, which can be overwhelming. Thus, it is important to effectively allocate their finite perceptual and cognitive resources for the most relevant information. This paper introduces a novel analysis tool that quantifies the amount of spatial and temporal information in time series of constant-elevation weather radar reflectivity images. The proposed Weather Radar Spatiotemporal Saliency (WR–STS) is based on the mathematical model of the human attention system (referred to as saliency) adapted to radar reflectivity images and makes use of information theory concepts. It is shown that WR-STS highlights spatially and temporally salient (attention attracting) regions in weather radar reflectivity images, which can be associated with meteorologically important regions. Its skill in highlighting current regions of interest is assessed by analyzing the WR-STS values within regions in which severe weather is likely to strike in the near future as defined by National Weather Service forecasters. The performance of WR-STS is demonstrated for a severe weather case and analyzed for a set of 10 diverse cases. Results support the hypothesis that WR-STS can identify regions with meteorologically important echoes and could assist in discerning fast-changing, highly structured weather echoes during complex severe weather scenarios, ultimately allowing forecasters to focus their attention and spend more time analyzing those regions.

Capturing the Signature of Severe Weather Events in Australia Using GPS Measurements

Rapid developments in satellite positioning, navigation, and timing have revolutionized surveying and mapping practice and significantly influenced the way people live and society operates. The advent of new generation global navigation satellite systems (GNSS) has heralded an exciting future for not only the GNSS community, but also many other areas that are critical to our society at large. With the rapid advances in space-based technologies and new dedicated space missions, the availability of large scale and dense contemporary GNSS networks such as regional continuously operating reference station (CORS) networks and the developments of new algorithms and methodologies, the ability of using space geodetic techniques to remotely sense the atmosphere (i.e., the troposphere and ionosphere) has dramatically improved. Real time GNSS-derived atmospheric variables with a high spatio-temporal resolution have become an important new source of measurements for meteorology, particularly for extreme weather events since water vapour (WV), as the most abundant element of greenhouse gas and accounting for $\sim $ 70% of global warming, is under-sampled in current meteorological and climate observing systems. This study investigates the emerging area of GNSS technology for near real-time monitoring and forecasting of severe weather and climate change research. This includes both ground-based global positioning system (GPS)-derived precipitable water vapour (PWV) estimation and four-dimensional (4-D) tomographic modeling for wet refractivity fields. Two severe weather case studies were used to investigate the signature of GPS-derived PWV and wet refractivity derived from the 4-D GPS tomographic model under the influence of severe mesoscale convective systems (MCSs). GPS observations from the Victorian state-wide CORS network, i.e., GPSnet, in Australia were used. Results showed strong spatial and temporal correlations between the variations in the ground-based GPS-derived PWV and the passage of the severe MCS. This indicates that the GPS method can complement conventional meteorological observations for the studying, monitoring, and potentially predicting of severe weather events. The advantage of using the ground-based GPS technique is that it can provide continuous observations for the storm passage with high temporal and spatial resolution. Results from these two case studies also suggest that GPS-derived PWV can resolve the synoptic signature of the dynamics and offer precursors to severe weather, and the tomographic technique has the potential to depict the three-dimensional (3-D) signature of wet refractivity for the convective and stratiform processes evident in MCS events. This research reveals the potential of using GNSS-derived PWV to strengthen numerical weather prediction (NWP) models and forecasts, and the potential for GNSS-derived PWV and wet refractivity fields to enhance early detection and sensing of severe weather.

A multisensor, blended, layered water vapor product for weather analysis and forecasting

This paper describes the creation of a near real-time, near-global, four-layer (surface–850, 850–700, 700– 500, and 500–300 hPa) blended layered water vapor (LWV) product using retrieved soundings from five polar-orbiting satellites [National Oceanic and Atmospheric Administration (NOAA)-18 and NOAA-19 satellites; Defense Meteorological Satellite Program F-18 satellite; Meteorological Operational satellite program’s Metop-A satellite; and National Atmospheric and Space Administration (NASA) Aqua satellite]. Both layer precipitable water (LPW) and layer relative humidity (LRH) are included in the blended LWV product. The NASA Atmospheric Infrared Sounder Version 6 retrieval product and NOAA Microwave Integrated Retrieval System soundings are used to create the product, and the use of retrieved water vapor profiles from multiple satellite inputs at different local times allows visualization of the flow of water vapor in layers. The product has advantages and complements geostationary water vapor imagery, derived products from geostationary satellites, and radiosondes in tracking moisture over data sparse regions in cloudy conditions. LPW profiles show absolute values at each layer of the column, while LRH profiles give a sense of whether the column is moistening or drying with height. Examples of the product are given for a severe weather case, the September 2013 Colorado Front Range floods, and a landfalling tropical depression. The usefulness of the product is discussed from the perspective of how tools commonly used by forecasters to analyze water vapor are augmented by the blended layered water vapor fields.

A Mediterranean nocturnal heavy rainfall and tornadic event. Part I: Overview, damage survey and radar analysis

This study presents an analysis of a severe weather case that took place during the early morning of the 2nd of November 2008, when intense convective activity associated with a rapidly evolving low pressure system affected the southern coast of Catalonia (NE Spain). The synoptic framework was dominated by an upper level trough and an associated cold front extending from Gibraltar along the Mediterranean coast of the Iberian Peninsula to SE France, which moved north-eastward. South easterly winds in the north of the Balearic Islands and the coast of Catalonia favoured high values of 0–3 km storm relative helicity which combined with moderate MLCAPE values and high shear favoured the conditions for organized convection. A number of multicell storms and others exhibiting supercell features, as indicated by Doppler radar observations, clustered later in a mesoscale convective system, and moved north-eastwards across Catalonia. They produced ground-level strong damaging wind gusts, an F2 tornado, hail and heavy rainfall. Total lightning activity (intra-cloud and cloud to ground flashes) was also relevant, exhibiting several classical features such as a sudden increased rate before ground level severe damage, as discussed in a companion study. Remarkable surface observations of this event include 24 h precipitation accumulations exceeding 100 mm in four different observatories and 30 minute rainfall amounts up to 40 mm which caused local flash floods. As the convective system evolved northward later that day it also affected SE France causing large hail, ground level damaging wind gusts and heavy rainfall.

Synergetic Use of Radar and Optical Satellite Images to Support Severe Storm Prediction for Offshore Wind Farming

In this paper, we show how satellite images taken by space-borne radar sensors can be used to determine mesoscale high-resolution wind fields in synergy with cloud parameters from optical data and, thus, help in the task of maintenance and planning offshore wind farms. The aim of this paper is to use synthetic aperture radar (SAR) and medium resolution imaging spectrometer (MERIS) onboard the environmental satellite (ENVISAT) in synergy to analyze severe weather systems, in particular, to describe the spatial evolution of the atmospheric boundary layer processes involved in cold air outbreaks. We investigated the fine-scale structure of a severe weather case on November 1, 2006 over the North Sea using satellite data. The satellite data are compared with numerical model results of the German Weather Service ldquoLokal Modellrdquo (LM) and the high-resolution limited area model (HIRLAM). LM and HIRLAM show differences in mesoscale turbulent behavior and coastal shadowing. Maximum wind speeds of up to 25 m/s are measured by SAR and are confirmed by the models. Significant differences are observed in the location of the maxima. High-resolution ENVISAT ASAR measurements provide very detailed information on small-scale atmospheric features, which seem to not be captured well by the analyzed numerical models, in particular, in coastal areas. Meteosat second generation (MSG) is used to determine the movement of cloud patterns. Cloud patterns seen in the optical data and radar cross-section modulation give a consistent dynamical picture of the atmospheric processes. The relevance for offshore wind farming is discussed.

Objective quantification of perturbations produced with a piecewise PV inversion technique

Abstract. PV inversion techniques have been widely used in numerical studies of severe weather cases. These techniques can be applied as a way to study the sensitivity of the responsible meteorological system to changes in the initial conditions of the simulations. Dynamical effects of a collection of atmospheric features involved in the evolution of the system can be isolated. However, aspects, such as the definition of the atmospheric features or the amount of change in the initial conditions, are largely case-dependent and/or subjectively defined. An objective way to calculate the modification of the initial fields is proposed to alleviate this problem. The perturbations are quantified as the mean absolute variations of the total energy between the original and modified fields, and an unique energy variation value is fixed for all the perturbations derived from different PV anomalies. Thus, PV features of different dimensions and characteristics introduce the same net modification of the initial conditions from an energetic point of view. The devised quantification method is applied to study the high impact weather case of 9–11 November 2001 in the Western Mediterranean basin, when a deep and strong cyclone was formed. On the Balearic Islands 4 people died, and sustained winds of 30 ms−1 and precipitation higher than 200 mm/24 h were recorded. Moreover, 700 people died in Algiers during the first phase of the event. The sensitivities to perturbations in the initial conditions of a deep upper level trough, the anticyclonic system related to the North Atlantic high and the surface thermal anomaly related to the baroclinicity of the environment are determined. Results reveal a high influence of the upper level trough and the surface thermal anomaly and a minor role of the North Atlantic high during the genesis of the cyclone.

Short-Range Forecast Impact from Assimilation of GPS-IPW Observations into the Rapid Update Cycle

Abstract Integrated precipitable water (IPW) estimates derived from time delays in the arrival of global positioning system (GPS) satellite signals are a relatively recent, high-frequency source of atmospheric moisture information available for real-time data assimilation. Different experimental versions of the Rapid Update Cycle (RUC) have assimilated these observations to assess GPS-IPW impact on moisture forecasts. In these tests, GPS-IPW data have proven to be a useful real-time source of moisture information, leading to more accurate short-range moisture forecasts when added to other observations. A multiyear experiment with parallel (one with GPS-IPW processed 24 h after the fact, one without) 3-h cycles using the original 60-km RUC was run from 1999 to 2004 with verification of each cycle against rawinsonde observations. This experiment showed a steady increase in the positive impact in short-range relative humidity (RH) forecasts due to the GPS-IPW data as the number of observing sites increased from 18 to almost 300 (as of 2004) across the United States and Canada. Positive impact from GPS-IPW on 850–700-hPa RH forecasts was also evident in 6- and 12-h forecasts. The impact of GPS-IPW data was also examined on forecasts from the more recent 20-km RUC, including a 1-h assimilation cycle and improved assimilation and physical parameterizations, now using real-time GPS-IPW retrievals available 30 min after valid time. In a 3-month comparison during the March–May 2004 period, 20-km RUC cycles with and without assimilation of GPS-IPW were compared with IPW for 3-, 6-, 9-, and 12-h forecasts. Using this measure, assimilation of GPS-IPW data led to the strongest improvements in the 3- and 6-h forecasts and smaller but still evident improvements in 9- and 12-h forecasts. In a severe convective weather case, inclusion of GPS-IPW data improved forecasts of convective available potential energy, an important predictor of severe storm potential, and relative humidity. Positive impact from GPS-IPW assimilation was found to vary over season, geographical location, and time of day, apparently related to variations in vertical mixing. For example, GPS-IPW has a stronger effect on improving RH forecasts at 850 hPa at nighttime (than daytime) and in cooler seasons (than warmer seasons) when surface moisture observations are less representative of conditions aloft. As a result of these studies, assimilation of GPS-IPW was added to the operational RUC run at NOAA/NCEP in June 2005 and to the operational North American Mesoscale model (also at NCEP) in June 2006 to improve their accuracy for short-range moisture forecasts.

Surface Data Assimilation Using an Ensemble Kalman Filter Approach with Initial Condition and Model Physics Uncertainties

Abstract The assimilation of surface observations using an ensemble Kalman filter (EnKF) approach is evaluated for the potential to improve short-range forecasting. Two severe weather cases are examined, in which the assimilation is performed over a 6-h period using hourly surface observations followed by an 18-h simulation period. Ensembles are created in three different ways—by using different initial and boundary conditions, by using different model physical process schemes, and by using both different initial and boundary conditions and different model physical process schemes. The ensembles are compared in order to investigate the role of uncertainties in the initial and boundary conditions and physical process schemes in EnKF data assimilation. In the initial condition ensemble, spread is associated largely with the displacement of atmospheric baroclinic systems. In the physics ensemble, spread comes from the differences in model physics, which results in larger spread in temperature and dewpoint temperature than the initial condition ensemble, and smaller spread in the wind fields. The combined initial condition and physics ensemble has properties from both of the previous two ensembles. It provides the largest spread and produces the best simulation for most of the variables, in terms of the rms difference between the ensemble mean and observations. Perhaps most importantly, this combined ensemble provides very good guidance on the mesoscale features important to the severe weather events of the day.