New operational nowcasting system at Finnish Meteorological Institute

Rapidly updating nowcasting system, Smartmet nowcast, has been developed at Finnish Meteorological Institute (FMI). The system combines information from multiple sources to operationally produce accurate and timely short range forecasts and a detailed description of the present weather to the end-users. The information sources combined are 1) Rapidly-updating high-resolution numerical weather prediction (NWP) MetCoOp nowcast (MNWC) forecast 2) radar-based nowcast 3) 10-day operational forecast. The Smartmet nowcast is currently produced for parameters 2-m temperature, 10-m wind speed, relative humidity, total cloud cover and accumulated 1-hour precipitation.The system produces hourly updating nowcast information over the Scandinavian forecast domain and combines it seamlessly with the 10-day operational forecast information. Prior the combination a simple bias correction scheme based on recent forecast error information is applied to MNWC model analysis and forecast fields of 2-m temperature, relative humidity and 10-m wind speed. The blending of the nowcast and the 10-day operational forecast information is done using Optical-flow based image morphing method, which provides visually seamless forecasts for each forecast variable.FMI has operationally produced Smartmet nowcast forecasts since September 2020. The validation of the data is in progress. The available results show that the Smartmet nowcast is improving the quality of short range forecasts and producing seamless and consistent forecasts. The method is also reducing the delay of forecast production. The Smartmet nowcast method will be automated in FMI forecast production in the near future.

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Adding to Fire Fighter Safety by Including Real-Time Radar Data in Short-Range Forecasts of Thunderstorm-Induced Wind Shifts

Fire ◽

10.3390/fire4030055 ◽

2021 ◽

Vol 4 (3) ◽

pp. 55

Author(s):

Gary L. Achtemeier ◽

Scott L. Goodrick

Keyword(s):

Real Time ◽

Short Range ◽

Prediction Models ◽

Weather Prediction ◽

Radar Data ◽

Mitigation Strategies ◽

Density Currents ◽

Gust Fronts ◽

Gust Front ◽

Range Forecasts

Abrupt changes in wind direction and speed caused by thunderstorm-generated gust fronts can, within a few seconds, transform slow-spreading low-intensity flanking fires into high-intensity head fires. Flame heights and spread rates can more than double. Fire mitigation strategies are challenged and the safety of fire crews is put at risk. We propose a class of numerical weather prediction models that incorporate real-time radar data and which can provide fire response units with images of accurate very short-range forecasts of gust front locations and intensities. Real-time weather radar data are coupled with a wind model that simulates density currents over complex terrain. Then two convective systems from formation and merger to gust front arrival at the location of a wildfire at Yarnell, Arizona, in 2013 are simulated. We present images of maps showing the progress of the gust fronts toward the fire. Such images can be transmitted to fire crews to assist decision-making. We conclude, therefore, that very short-range gust front prediction models that incorporate real-time radar data show promise as a means of predicting the critical weather information on gust front propagation for fire operations, and that such tools warrant further study.

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Existence of multiple scales in uncertainty of numerical weather prediction

Scientific Reports ◽

10.1038/s41598-019-52157-x ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Hyo-Jong Song

Keyword(s):

Numerical Weather Prediction ◽

Short Range ◽

Multiple Scales ◽

Forecast Error ◽

Weather Prediction ◽

Initial Condition ◽

Forecast Uncertainty ◽

Essential Information ◽

Numerical Weather ◽

Sample Correlation

Abstract Numerical weather prediction provides essential information of societal influence. Advances in the initial condition estimation have led to the improvement of the prediction skill. The process to produce the better initial condition (analysis) with the combination of short-range forecast and observation over the globe requires information about uncertainty of the forecast results to decide how much observation is reflected to the analysis and how far the observation information should be propagated. Forecast ensemble represents the error of the short-range forecast at the instance. The influence of observation propagating along with forecast ensemble correlation needs to be restricted by localized correlation function because of less reliability of sample correlation. So far, solitary radius of influence is usually used since there has not been an understanding about the realism of multiple scales in the forecast uncertainty. In this study, it is explicitly shown that multiple scales exist in short-range forecast error and any single-scale localization approach could not resolve this situation. A combination of Gaussian correlation functions of various scales is designed, which more weighs observation itself near the data point and makes ensemble perturbation, far from the observation position, more participate in decision of the analysis. Its outstanding performance supports the existence of multi-scale correlation in forecast uncertainty.

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Impact Assessment of Daily Satellite-Derived Surface Albedo in a Limited-Area NWP Model

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-11-0163.1 ◽

2012 ◽

Vol 51 (10) ◽

pp. 1835-1854 ◽

Cited By ~ 9

Author(s):

Jure Cedilnik ◽

Dominique Carrer ◽

Jean-François Mahfouf ◽

Jean-Louis Roujean

Keyword(s):

Land Surface ◽

Short Range ◽

Surface Albedo ◽

Weather Prediction ◽

A Priori ◽

Limited Area ◽

Model Biases ◽

Nwp Model ◽

The Impact ◽

Range Forecasts

AbstractThis study examines the impact of daily satellite-derived albedos on short-range forecasts in a limited-area numerical weather prediction (NWP) model over Europe. Contrary to previous studies in which satellite products were used to derive monthly “climatologies,” a daily surface (snow free) albedo is analyzed by a Kalman filter. The filter combines optimally a satellite product derived from the Meteosat Second Generation geostationary satellite [and produced by the Land Surface Analyses–Satellite Application Facility of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)], an albedo climatology, and a priori information given by “persistence.” The surface albedo analyzed for a given day is used as boundary conditions of the NWP model to run forecasts starting the following day. Results from short-range forecasts over a 1-yr period reveal the capacity of satellite information to reduce model biases and RMSE in screen-level temperature (during daytime and intermediate seasons). The impact on forecast scores is larger when considering the analyzed surface albedo rather than another climatologically based albedo product. From comparisons with measurements from three flux-tower stations over mostly homogeneous French forests, it is seen that the model biases in surface net radiation are significantly reduced. An impact on the whole planetary boundary layer, particularly in summer, results from the use of an observed surface albedo. An unexpected behavior produced in summer by the satellite-derived albedo on surface temperature is also explained. The forecast runs presented here, performed in dynamical adaptation mode, will be complemented later on by data assimilation experiments over typically monthly periods.

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Hourly and Daily Reference Evapotranspiration with ASCE-PM Model for Paraná State, Brazil

Revista Brasileira de Meteorologia ◽

10.1590/0102-77863610009 ◽

2021 ◽

Author(s):

Aline Aparecida dos Santos ◽

Jorge Luiz Moretti de Souza ◽

Stefanie Lais Kreutz Rosa

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Reference Evapotranspiration ◽

Mean Square ◽

Average Difference ◽

Climate Type ◽

Good Potential ◽

Index Of Agreement ◽

Soil And Water ◽

Meteorological Institute

Abstract The objective of this study was to verify the magnitude and trend of hourly reference evapotranspiration (EToh), as well as associate and analyze daily ETo (ETod) series and the sum of hourly ETo (ETo24h) in 24 h, estimated with the Penman-Monteith ASCE model for Paraná State (Cfa and Cfb climate type). Relative humidity (RH), temperature (T), solar radiation (Rs) and wind speed (u2) data were obtained from 25 meteorological stations from the National Meteorological Institute (INMET), between December 1, 2016 to November 8, 2018. The analyzes were performed by linear regression and associations considering the root mean square error, correlation coefficient and index of agreement. The EToh trend has a Gaussian distribution, with the highest values between 12:00 p.m. and 2:00 p.m., with the maximum average being 0.44 mm h−1 (Cfa climate type) and 0.35 mm h−1 (Cfb climate type). The average difference between the ETo24h and ETod values was small (5.1% for Cfa and 7.4% for Cfb), resulting in close linear associations. The results obtained indicate that EToh has good potential to be used in planning and management in the field of soil and water engineering, in Paraná State.

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A method to estimate freezing rain climatology from ERA-Interim reanalysis over Europe

10.5194/nhess-2016-225 ◽

2016 ◽

Author(s):

Matti Kämäräinen ◽

Otto Hyvärinen ◽

Kirsti Jylhä ◽

Andrea Vajda ◽

Simo Neiglick ◽

...

Keyword(s):

Cross Validation ◽

Critical Infrastructure ◽

Weather Prediction ◽

Reanalysis Data ◽

Vertical Profiles ◽

Freezing Rain ◽

Finnish Meteorological Institute ◽

Precipitation Type ◽

Safety Assessments ◽

Meteorological Institute

Abstract. A method for estimating the occurrence of freezing rain (FZRA) in gridded atmospheric datasets was evaluated, calibrated against SYNOP weather station observations, and applied to the ERA-Interim reanalysis for climatological studies of the phenomenon. The algorithm, originally developed for detecting the precipitation type in numerical weather prediction at the Finnish Meteorological Institute, uses vertical profiles of relative humidity and temperature as input. Reanalysis data in 6-hourly time resolution was analyzed over Europe for the period 1979–2014. Mean annual and monthly numbers of FZRA events, as well as probabilities of duration and spatial extent of events, were then derived. The algorithm was able to reproduce accurately the observed, spatially averaged interannual variability of FZRA (correlation = 0.93) during the 36-year period, but at station level rather low validation and cross-validation statistics were achieved (mean correlation = 0.44). Coarse grid resolution of the reanalysis, and misclassifications to other freezing phenomena in SYNOP observations, such as ice pellets and freezing drizzle, contribute to the low validation results at station scale. Although the derived gridded climatology is preliminary, it may be useful, for example, in safety assessments of critical infrastructure.

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The Inclusion of IWV Estimates from AIRS/AMSU and SSM/I Sensors into the CPTEC/INPE Global Data Assimilation System

Monthly Weather Review ◽

10.1175/mwr-d-10-05061.1 ◽

2013 ◽

Vol 141 (1) ◽

pp. 93-111

Author(s):

Luiz F. Sapucci ◽

Dirceu L. Herdies ◽

Renata W. B. Mendonça

Keyword(s):

Water Vapor ◽

Data Assimilation ◽

Southern Hemisphere ◽

Short Range ◽

Weather Forecasting ◽

Weather Prediction ◽

The Impact ◽

Data Assimilation System ◽

Assimilation System ◽

Range Forecasts

Abstract Water vapor plays a crucial role in atmospheric processes and its distribution is associated with cloud-cover fraction and rainfall. The inclusion of integrated water vapor (IWV) estimates in numerical weather prediction improves the vertical structure of the humidity analysis and consequently contributes to obtaining a more realistic atmospheric state. Currently, satellite remote sensing is the most important source of humidity measurements in the Southern Hemisphere, providing information with good horizontal resolution and global coverage. In this study, the inclusion of IWV retrieved from the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A (AIRS/AMSU) and Special Sensor Microwave Imager (SSM/I) were investigated as additional information in the Physical-space Statistical Analysis System (PSAS), which is the operational data assimilation system at the Center for Weather Forecasting and Climate Studies of the Brazilian National Institute for Space Research (CPTEC/INPE). Experiments were carried out with and without the assimilation of IWV values from both sensors. Results show that, in general, the IWV assimilation reduces the error in short-range forecasts of humidity profile, particularly over tropical regions. In these experiments, an analysis of the impact of the inclusion of IWV values from SSM/I and AIRS/AMSU sensors was done. Results indicated that the impact of the SSM/I values is significant over high-latitude oceanic regions in the Southern Hemisphere, while the impact of AIRS/AMSU values is more significant over continental regions where surface measurements are scarce, such as the Amazonian region. In that area the assimilation of IWV values from the AIRS/AMSU sensor shows a tendency to reduce the overestimate of the precipitation in short-range forecasts.

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Determining Weather Radar Antenna Pointing Using Signals Detected from the Sun at Low Antenna Elevations

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech1978.1 ◽

2007 ◽

Vol 24 (3) ◽

pp. 476-483 ◽

Cited By ~ 27

Author(s):

Asko Huuskonen ◽

Iwan Holleman

Keyword(s):

Relative Humidity ◽

Radio Wave ◽

Simplified Model ◽

The Sun ◽

Radio Waves ◽

Atmospheric Attenuation ◽

Atmospheric Refraction ◽

Finnish Meteorological Institute ◽

Radar Antenna ◽

Meteorological Institute

Abstract A method to determine the elevation and azimuth biases of the radar antenna using solar signals observed by a scanning radar is presented. Data recorded at low elevation angles where the atmospheric refraction has a significant effect on the propagation of the radio wave are used, and a method to take the effect of the refraction into account in the analysis is presented. A set of equations is given by which the refraction of the radio waves as a function of the relative humidity can easily be calculated. Also, a simplified model for the calculation of the atmospheric attenuation is presented. The consistency of the adopted models for the atmospheric refraction and atmospheric attenuation is confirmed by data collected at a single elevation pointing, but over a long observing time. Finally, the method is applied to datasets based on operational measurements at the Finnish Meteorological Institute (FMI) and Royal Netherlands Meteorological Institute (KNMI), and elevation and azimuth biases of the radars are shown.

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Variational Bias Correction of TAMDAR Temperature Observations in the WRF Data Assimilation System

Monthly Weather Review ◽

10.1175/mwr-d-18-0025.1 ◽

2019 ◽

Vol 147 (6) ◽

pp. 1927-1945

Author(s):

Feng Gao ◽

Zhiquan Liu ◽

Juhui Ma ◽

Neil A. Jacobs ◽

Peter P. Childs ◽

...

Keyword(s):

Mach Number ◽

Data Assimilation ◽

Bias Correction ◽

Short Range ◽

Forecast Error ◽

Meteorological Data ◽

Precipitation Event ◽

Average Error ◽

Bias Model ◽

Range Forecasts

Abstract A variational bias correction (VarBC) scheme is developed and tested using regional Weather Research and Forecasting Model Data Assimilation (WRFDA) to correct systematic errors in aircraft-based measurements of temperature produced by the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) system. Various bias models were investigated, using one or all of aircraft height tendency, Mach number, temperature tendency, and the observed temperature as predictors. These variables were expected to account for the representation of some well-known error sources contributing to uncertainties in TAMDAR temperature measurements. The parameters corresponding to these predictors were evolved in the model for a two-week period to generate initial estimates according to each unique aircraft tail number. Sensitivity experiments were then conducted for another one-month period. Finally, a case study using VarBC of a cold front precipitation event is examined. The implementation of VarBC reduces biases in TAMDAR temperature innovations. Even when using a bias model containing a single predictor, such as height tendency or Mach number, the VarBC produces positive impacts on analyses and short-range forecasts of temperature with smaller standard deviations and biases than the control run. Additionally, by employing a multiple-predictor bias model, which describes the statistical relations between innovations and predictors, and uses coefficients to control the evolution of components in the bias model with respect to their reference values, VarBC further reduces the average error of analyses and short-range forecasts with respect to observations. The potential impacts of VarBC on precipitation forecasts were evaluated, and the VarBC is able to indirectly improve the prediction of precipitation location by reducing the forecast error for wind-related synoptic circulation leading to precipitation.

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A method to estimate freezing rain climatology from ERA-Interim reanalysis over Europe

Natural Hazards and Earth System Science ◽

10.5194/nhess-17-243-2017 ◽

2017 ◽

Vol 17 (2) ◽

pp. 243-259 ◽

Cited By ~ 10

Author(s):

Matti Kämäräinen ◽

Otto Hyvärinen ◽

Kirsti Jylhä ◽

Andrea Vajda ◽

Simo Neiglick ◽

...

Keyword(s):

Cross Validation ◽

Critical Infrastructure ◽

Weather Prediction ◽

Reanalysis Data ◽

Data Sets ◽

Freezing Rain ◽

Finnish Meteorological Institute ◽

Precipitation Type ◽

Safety Assessments ◽

Meteorological Institute

Abstract. A method for estimating the occurrence of freezing rain (FZRA) in gridded atmospheric data sets was evaluated, calibrated against SYNOP weather station observations, and applied to the ERA-Interim reanalysis for climatological studies of the phenomenon. The algorithm, originally developed at the Finnish Meteorological Institute for detecting the precipitation type in numerical weather prediction, uses vertical profiles of relative humidity and temperature as input. Reanalysis data in 6 h time resolution were analysed over Europe for the period 1979–2014. Mean annual and monthly numbers of FZRA events, as well as probabilities of duration and spatial extent of events, were then derived. The algorithm was able to accurately reproduce the observed, spatially averaged interannual variability of FZRA (correlation 0.90) during the 36-year period, but at station level rather low validation and cross-validation statistics were achieved (mean correlation 0.38). Coarse-grid resolution of the reanalysis and misclassifications to other freezing phenomena in SYNOP observations, such as ice pellets and freezing drizzle, contribute to the low validation results at station level. Although the derived gridded climatology is preliminary, it may be useful, for example, in safety assessments of critical infrastructure.

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A Deep Learning Method for Short-Range Point Forecasts of Wind Speed

10.5194/egusphere-egu2020-4434 ◽

2020 ◽

Author(s):

Petrina Papazek ◽

Irene Schicker

Keyword(s):

Deep Learning ◽

Wind Speed ◽

Short Range ◽

Weather Prediction ◽

Machine Learning Algorithms ◽

Heterogeneous Structure ◽

Gridded Data ◽

Medium Range Weather Forecast ◽

Grid Points ◽

Meteorological Observations

In this study, we present a deep learning-based method to provide short-range point-forecasts (1-2 days ahead) of the 10-meter wind speed for complex terrain. Gridded data with different horizontal resolutions from numeric weather prediction (NWP) models, gridded observations, and point data are used. An artificial neural network (ANN), able to process several differently structured inputs simultaneously, is developed. The heterogeneous structure of inputs is targeted by the ANN by combining convolutional, long-short-term-memory (LSTM), fully connected (FC) layers, and others within a common network. Convolutional layers efficiently solve image processing tasks, however, they are applicable to any gridded data source. An LSTM layer models recurrent steps in the ANN and is, thus, useful for time-series, such as meteorological observations. Further key objectives of this research are to consider different spatial and temporal resolutions and different topographic characteristics of the selected sites.Data from the Austrian TAWES system (Teilautomatische Wetterstationen, meteorological observations in 10-minute intervals), INCA's (Integrated Nowcasting through Comprehensive Analysis) gridded observation fields, and NWP data from the ECMWF IFS (European Center for Medium-Range Weather Forecast&#8217;s Integrated Forecasting System) model are used in this study. Hourly runs for 12 test locations (selected TAWES sites representing different topographic &#160;characteristics in Austria) and different seasons are conducted. &#160; The ANN&#8217;s results yield, in general, high forecast-skills (MAE=1.13 m/s, RMSE=1.72 m/s), indicating a successful learning based on the used training data. Different combinations of the number of input field grid points were investigated centering around the target sites. It is shown that a small number of ECMWF IFS grid Points (e.g.: 5x5 grid points) and a higher number of INCA grid points (e.g.: 15x15) resulted in the best performing forecasts. The different number of grid points is directly related to the models' resolution. However, keeping the nowcasting-range in mind, it is shown that adding NWP data does not increase the model performance. Thus, for nowcasting a stronger weighting towards the observations is important. Beyond the nowcasting range, the deep learning-based ANN model outperforms the more basic machine learning algorithms as well as other alternative models.

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