scholarly journals The Impact of Low Latency Satellite Sounder Observations on Local Severe Storm Forecasts in Regional NWP

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 650 ◽  
Author(s):  
Pei Wang ◽  
Jun Li ◽  
Timothy J. Schmit
Keyword(s):  
Positive Impact ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Advanced Technology ◽  
Low Latency ◽  
Telecommunication System ◽  
Global Telecommunication System ◽  
Direct Broadcast ◽  
The Impact ◽  
Data Latency

The forecasts of local severe storms (LSS) are highly dependent on how well the pre-convection environment is characterized in the numerical weather prediction (NWP) model analysis. The usefulness of the forecasts is highly dependent on how frequently the forecast is updated. Therefore, the data latency is critical for assimilation into regional NWP models for it to be able to assimilate more data within the data cut-off window. These low latency data can be obtained through direct broadcast sites and direct receiving systems. Observing system experiments (OSE) were performed to study the impact of data latency on the LSS forecasts. The experiments assimilated all existing observations including conventional data (from the global telecommunication system, GTS) and satellite sounder radiance data (AMSU-A (The Advanced Microwave Sounding Unit-A), ATMS (Advanced Technology Microwave Sounder), CrIS (Cross-track Infrared Sounder), and IASI (Infrared Atmospheric Sounding Interferometer)). They were carried out in a nested domain with a horizontal resolution of 9 km and 3 km in the weather research and forecasting (WRF) model. The forecast quality scores of the LSS precipitation forecasts were calculated and compared with different data cut-off widows to evaluate the impact of data latency. The results showed that low latency can lead to an improved and positive impact on precipitation and other forecasts, which indicates the potential application of LEO direct broadcast (DB) data in a high-resolution regional NWP for LSS forecasts.

scholarly journals Impacts of Assimilating ATMS Radiances on Heavy Rainfall Forecast in RMAPS-ST

2020 ◽  
Vol 12 (7) ◽  
pp. 1147
Author(s):  
Yanhui Xie ◽  
Min Chen ◽  
Jiancheng Shi ◽  
Shuiyong Fan ◽  
Jing He ◽  
...  
Keyword(s):  
Heavy Rainfall ◽  
Initial Conditions ◽  
Positive Impact ◽  
Weather Prediction ◽  
Advanced Technology ◽  
Added Value ◽  
Rainfall Forecast ◽  
Hourly Rainfall ◽  
Radiance Data ◽  
The Impact

The Advanced Technology Microwave Sounder (ATMS) mounted on the Suomi National Polar-Orbiting Partnership (NPP) satellite can provide both temperature and humidity information for a weather prediction model. Based on the rapid-refresh multi-scale analysis and prediction system—short-term (RMAPS-ST), we investigated the impact of ATMS radiance data assimilation on strong rainfall forecasts. Two groups of experiments were conducted to forecast heavy precipitation over North China between 18 July and 20 July 2016. The initial conditions and forecast results from the two groups of experiments have been compared and evaluated against observations. In comparison with the first group of experiments that only assimilated conventional observations, some added value can be obtained for the initial conditions of temperature, humidity, and wind fields after assimilating ATMS radiance observations in the system. For the forecast results with the assimilation of ATMS radiances, the score skills of quantitative forecast rainfall have been improved when verified against the observed rainfall. The Heidke skill score (HSS) skills of 6-h accumulated precipitation in the 24-h forecasts were overall increased, more prominently so for the heavy rainfall above 25 mm in the 0–6 h of forecasts. Assimilating ATMS radiance data reduced the false alarm ratio of quantitative precipitation forecasting in the 0–12 h of the forecast range and thus improved the threat scores for the heavy rainfall storm. Furthermore, the assimilation of ATMS radiances improved the spatial distribution of hourly rainfall forecast with observations compared with that of the first group of experiments, and the mean absolute error was reduced in the 10-h lead time of forecasts. The inclusion of ATMS radiances provided more information for the vertical structure of features in the temperature and moisture profiles, which had an indirect positive impact on the forecasts of the heavy rainfall in the RMAPS-ST system. However, the deviation in the location of the heavy rainfall center requires future work.

scholarly journals Met Office Unified Model Tropical Cyclone Performance Following Major Changes to the Initialization Scheme and a Model Upgrade

2016 ◽  
Vol 31 (5) ◽  
pp. 1433-1449 ◽  
Author(s):  
Julian T. Heming
Keyword(s):  
Prediction Models ◽  
Positive Impact ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Unified Model ◽  
Track Forecast ◽  
Forecast Errors ◽  
Forecast Bias ◽  
Initialization Scheme ◽  
The Impact

Abstract The Met Office has used various schemes to initialize tropical cyclones (TCs) in its numerical weather prediction models since the 1980s. The scheme introduced in 1994 was particularly successful in reducing track forecast errors in the model. Following modifications in 2007 the scheme was still beneficial, although to a lesser degree than before. In 2012 a new trial was conducted that showed that the scheme now had a detrimental impact on TC track forecasts. As a consequence of this, the scheme was switched off. The Met Office Unified Model (MetUM) underwent a major upgrade in 2014 including a new dynamical core, changes to the model physics, an increase in horizontal resolution, and changes to satellite data usage. An evaluation of the impact of this change on TC forecasts found a positive impact both on track and particularly intensity forecasts. Following implementation of the new model formulation in 2014, a new scheme for initialization of TCs in the MetUM was developed that involved the assimilation of central pressure estimates from TC warning centers. A trial showed that this had a positive impact on both track and intensity predictions from the model. Operational results from the MetUM in 2014 and 2015 showed that the combined impact of the model upgrade and new TC initialization scheme was a dramatic cut in both TC track forecast errors and intensity forecast bias.

scholarly journals Lagrangian gravity wave spectra in the lower stratosphere of current (re)analyses

2020 ◽  
Vol 20 (15) ◽  
pp. 9331-9350 ◽  
Author(s):  
Aurélien Podglajen ◽  
Albert Hertzog ◽  
Riwal Plougonven ◽  
Bernard Legras
Keyword(s):  
Gravity Wave ◽  
Gravity Waves ◽  
Lower Stratosphere ◽  
Weather Prediction ◽  
Low Frequency ◽  
Horizontal Resolution ◽  
Vertical Wind ◽  
Temperature Fields ◽  
Telecommunication System ◽  
Global Telecommunication System

Abstract. Due to their increasing spatial resolution, numerical weather prediction (NWP) models and the associated analyses resolve a growing fraction of the gravity wave (GW) spectrum. However, it is unclear how well this “resolved” part of the spectrum truly compares to the actual atmospheric variability. In particular, the Lagrangian variability, relevant, for example, to atmospheric dispersion and to microphysical modeling in the upper troposphere–lower stratosphere (UTLS), has not yet been documented in recent products. To address this shortcoming, this paper presents an assessment of the GW spectrum as a function of the intrinsic (air parcel following) frequency in recent (re)analyses (ERA-Interim, ERA5, the ECMWF operational analysis and MERRA-2). Long-duration, quasi-Lagrangian balloon observations in the equatorial and Antarctic lower stratosphere are used as a reference for the atmospheric spectrum and are compared to synthetic balloon observations along trajectories calculated using the wind and temperature fields of the reanalyses. Overall, the reanalyses represent realistic features of the spectrum, notably the spectral gap between planetary and gravity waves and a peak in horizontal kinetic energy associated with inertial waves near the Coriolis frequency f in the polar region. In the tropics, they represent the slope of the spectrum at low frequency. However, the variability is generally underestimated even in the low-frequency portion of the spectrum. In particular, the near-inertial peak, although present in the reanalyses, has a reduced magnitude compared to balloon observations. We compare the observed and modeled variabilities of temperature, zonal momentum flux and vertical wind speed, which are related to low-, mid- and high-frequency waves, respectively. The probability density function (PDF) distributions have similar shapes but show increasing disagreement with increasing intrinsic frequency. Since at those altitudes they are mainly caused by gravity waves, we also compare the geographic distribution of vertical wind fluctuations in the different products, which emphasizes the increase of both GW variance and intermittency with horizontal resolution. Finally, we quantify the fraction of resolved variability and its dependency on model resolution for the different variables. In all (re)analysis products, a significant part of the variability is still missing, especially at high frequencies, and should hence be parameterized. Among the two polar balloon datasets used, one was broadcast on the Global Telecommunication System for assimilation in NWP models, while the other consists of independent observations (unassimilated in the reanalyses). Comparing the Lagrangian spectra between the two campaigns shows that the (re)analyses are largely influenced by balloon data assimilation, which especially enhances the variance at low GW frequency.

scholarly journals Impact of the assimilation of lightning data on the precipitation forecast at different forecast ranges

2017 ◽  
Vol 14 ◽  
pp. 187-194 ◽  
Author(s):  
Stefano Federico ◽  
Marco Petracca ◽  
Giulia Panegrossi ◽  
Claudio Transerici ◽  
Stefano Dietrich
Keyword(s):  
Data Assimilation ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Time Range ◽  
False Alarms ◽  
Forecast Time ◽  
The Impact

Abstract. This study investigates the impact of the assimilation of total lightning data on the precipitation forecast of a numerical weather prediction (NWP) model. The impact of the lightning data assimilation, which uses water vapour substitution, is investigated at different forecast time ranges, namely 3, 6, 12, and 24 h, to determine how long and to what extent the assimilation affects the precipitation forecast of long lasting rainfall events (> 24 h). The methodology developed in a previous study is slightly modified here, and is applied to twenty case studies occurred over Italy by a mesoscale model run at convection-permitting horizontal resolution (4 km). The performance is quantified by dichotomous statistical scores computed using a dense raingauge network over Italy. Results show the important impact of the lightning assimilation on the precipitation forecast, especially for the 3 and 6 h forecast. The probability of detection (POD), for example, increases by 10 % for the 3 h forecast using the assimilation of lightning data compared to the simulation without lightning assimilation for all precipitation thresholds considered. The Equitable Threat Score (ETS) is also improved by the lightning assimilation, especially for thresholds below 40 mm day−1. Results show that the forecast time range is very important because the performance decreases steadily and substantially with the forecast time. The POD, for example, is improved by 1–2 % for the 24 h forecast using lightning data assimilation compared to 10 % of the 3 h forecast. The impact of the false alarms on the model performance is also evidenced by this study.

scholarly journals Effect of the uncertainty in meteorology on air quality model predictions

Időjárás ◽  
2021 ◽  
Vol 125 (4) ◽  
pp. 625-646
Author(s):  
Zita Ferenczi ◽  
Emese Homolya ◽  
Krisztina Lázár ◽  
Anita Tóth
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Model System ◽  
Air Quality Model ◽  
Quality Model ◽  
Near Surface ◽  
The Impact

An operational air quality forecasting model system has been developed and provides daily forecasts of ozone, nitrogen oxides, and particulate matter for the area of Hungary and three big cites of the country (Budapest, Miskolc, and Pécs). The core of the model system is the CHIMERE off-line chemical transport model. The AROME numerical weather prediction model provides the gridded meteorological inputs for the chemical model calculations. The horizontal resolution of the AROME meteorological fields is consistent with the CHIMERE horizontal resolution. The individual forecasted concentrations for the following 2 days are displayed on a public website of the Hungarian Meteorological Service. It is essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input meteorological fields. The main aim of this research is to probe the response of an air quality model to its uncertain meteorological inputs. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. During the past decades, meteorological ensemble modeling has received extensive research and operational interest because of its ability to better characterize forecast uncertainty. One such ensemble forecast system is the one of the AROME model, which has an 11-member ensemble where each member is perturbed by initial and lateral boundary conditions. In this work we focus on wintertime particulate matter concentrations, since this pollutant is extremely sensitive to near-surface mixing processes. Selecting a number of extreme air pollution situations we will show what the impact of the meteorological uncertainty is on the simulated concentration fields using AROME ensemble members.

scholarly journals Comparing the cloud vertical structure derived from several methods based on radiosonde profiles and ground-based remote sensing measurements

2014 ◽  
Vol 7 (8) ◽  
pp. 2757-2773 ◽  
Author(s):  
M. Costa-Surós ◽  
J. Calbó ◽  
J. A. González ◽  
C. N. Long
Keyword(s):  
Remote Sensing ◽  
Vertical Structure ◽  
Cloud Layer ◽  
Cloud Base ◽  
Low Resolution ◽  
Telecommunication System ◽  
Perfect Agreement ◽  
Data Set ◽  
Global Telecommunication System ◽  
The Impact

Abstract. The cloud vertical distribution and especially the cloud base height, which is linked to cloud type, are important characteristics in order to describe the impact of clouds on climate. In this work, several methods for estimating the cloud vertical structure (CVS) based on atmospheric sounding profiles are compared, considering the number and position of cloud layers, with a ground-based system that is taken as a reference: the Active Remote Sensing of Clouds (ARSCL). All methods establish some conditions on the relative humidity, and differ in the use of other variables, the thresholds applied, or the vertical resolution of the profile. In this study, these methods are applied to 193 radiosonde profiles acquired at the Atmospheric Radiation Measurement (ARM) Southern Great Plains site during all seasons of the year 2009 and endorsed by Geostationary Operational Environmental Satellite (GOES) images, to confirm that the cloudiness conditions are homogeneous enough across their trajectory. The perfect agreement (i.e., when the whole CVS is estimated correctly) for the methods ranges between 26 and 64%; the methods show additional approximate agreement (i.e., when at least one cloud layer is assessed correctly) from 15 to 41%. Further tests and improvements are applied to one of these methods. In addition, we attempt to make this method suitable for low-resolution vertical profiles, like those from the outputs of reanalysis methods or from the World Meteorological Organization's (WMO) Global Telecommunication System. The perfect agreement, even when using low-resolution profiles, can be improved by up to 67% (plus 25% of the approximate agreement) if the thresholds for a moist layer to become a cloud layer are modified to minimize false negatives with the current data set, thus improving overall agreement.

scholarly journals Effects of Increasing Horizontal Resolution in a Convection-Permitting Model on Flood Forecasting: The 2011 Dramatic Events in Liguria, Italy

2015 ◽  
Vol 16 (4) ◽  
pp. 1843-1856 ◽  
Author(s):  
Silvio Davolio ◽  
Francesco Silvestro ◽  
Piero Malguzzi
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Flood Forecasting ◽  
Horizontal Resolution ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Flood Prediction ◽  
Meteorological Model ◽  
The Impact ◽  
Atmospheric Simulations

Abstract Coupling meteorological and hydrological models is a common and standard practice in the field of flood forecasting. In this study, a numerical weather prediction (NWP) chain based on the BOLogna Limited Area Model (BOLAM) and the MOdello LOCale in Hybrid coordinates (MOLOCH) was coupled with the operational hydrological forecasting chain of the Ligurian Hydro-Meteorological Functional Centre to simulate two major floods that occurred during autumn 2011 in northern Italy. Different atmospheric simulations were performed by varying the grid spacing (between 1.0 and 3.0 km) of the high-resolution meteorological model and the set of initial/boundary conditions driving the NWP chain. The aim was to investigate the impact of these parameters not only from a meteorological perspective, but also in terms of discharge predictions for the two flood events. The operational flood forecasting system was thus used as a tool to validate in a more pragmatic sense the quantitative precipitation forecast obtained from different configurations of the NWP system. The results showed an improvement in flood prediction when a high-resolution grid was employed for atmospheric simulations. In turn, a better description of the evolution of the precipitating convective systems was beneficial for the hydrological prediction. Although the simulations underestimated the severity of both floods, the higher-resolution model chain would have provided useful information to the decision-makers in charge of protecting citizens.

scholarly journals Development of a hybrid variational-ensemble data assimilation technique for observed lightning tested in a mesoscale model

2014 ◽  
Vol 21 (5) ◽  
pp. 1027-1041 ◽  
Author(s):  
K. Apodaca ◽  
M. Zupanski ◽  
M. DeMaria ◽  
J. A. Knaff ◽  
L. D. Grasso
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Positive Impact ◽  
Weather Prediction ◽  
New Information ◽  
Forecasting System ◽  
Maximum Likelihood Ensemble Filter ◽  
The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Geostationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), and improving initial conditions during several data assimilation cycles. However, the 6 h forecast after the assimilation did not show a clear improvement in terms of root mean square (RMS) errors.

scholarly journals An Assessment of Data from the Advanced Technology Microwave Sounder at the Met Office

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Amy Doherty ◽  
Nigel Atkinson ◽  
William Bell ◽  
Andrew Smith
Keyword(s):  
Weather Prediction ◽  
Advanced Technology ◽  
Tropospheric Temperature ◽  
Noise Performance ◽  
Sea Level Pressure ◽  
Day Range ◽  
Office System ◽  
The Impact ◽  
Summary Of Performance ◽  
Analysis Errors

An appraisal of the Advanced Technology Microwave Sounder (ATMS) for use in numerical weather prediction (NWP) is presented, including an assessment of the data quality, the impact on Met Office global forecasts in preoperational trials, and a summary of performance over a period of 17 months operational use. After remapping, the noise performance (NEΔT) of the tropospheric temperature sounding channels is evaluated to be approximately 0.1 K, comparing favourably with AMSU-A. However, the noise is not random, differences between observations and simulations based on short-range forecast fields show a spurious striping effect, due to 1/f noise in the receiver. The amplitude of this signal is several tenths of a Kelvin, potentially a concern for NWP applications. In preoperational tests, adding ATMS data to a full Met Office system already exploiting data from four microwave sounders improves southern hemisphere mean sea level pressure forecasts in the 2- to 5-day range by 1-2%. In operational use, where data from five other microwave sounders is assimilated, forecast impact is typically between −0.05 and −0.1 J/kg (3.4% of total mean impact per day over the period 1 April to 31 July 2013). This suggests benefits beyond redundancy, associated with reducing already small analysis errors.

Improved impacts in observing system simulation experiments of radio occultation observations as a result of model and data assimilation changes

2020 ◽  
Author(s):  
L. CUCURULL ◽  
S. P. F. CASEY
Keyword(s):  
Data Assimilation ◽  
Radio Occultation ◽  
Weather Forecasting ◽  
Positive Impact ◽  
System Simulation ◽  
Weather Prediction ◽  
Wind Fields ◽  
Simulation Experiments ◽  
The Impact ◽  
Forward Operator

AbstractAs global data assimilation systems continue to evolve, Observing System Simulation Experiments (OSSEs) need to be updated to accurately quantify the impact of proposed observing technologies in weather forecasting. Earlier OSSEs with radio occultation (RO) observations have been updated and the impact of the originally proposed Constellation Observing Satellites for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) mission, with a high-inclination and low-inclination component, has been investigated by using the operational data assimilation system at NOAA and a 1-dimensional bending angle RO forward operator. It is found that the impact of the low-inclination component of the originally planned COSMIC-2 mission (now officially named COSMIC-2) has significantly increased as compared to earlier studies, and significant positive impact is now found globally in terms of mass and wind fields. These are encouraging results as COSMIC-2 was successfully launched in June 2019 and data have been recently released to operational weather centers. Earlier findings remain valid indicating that globally distributed RO observations are more important to improve weather prediction globally than a denser sampling of the tropical latitudes. Overall, the benefits reported here from assimilating RO soundings are much more significant than the impacts found in previous OSSEs. This is largely attributed to changes in the data assimilation and forecast system and less to the more advanced 1-dimensional forward operator chosen for the assimilation of RO observations.

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