Meso-Meteorology and Very Short-Range Forecasting (VSRF)
Hydrometeorological equipment for short range weather forecasting used in Russia Russian State Hydrometeorological University Prof. Kuznetsov Anatoly Dmitrievitch Prof. Solonin Alexander Sergeevich Definitions of very short range weather forecasting and nowcasting Forecasting with a lead time less than 12 hours is very short range forecasting 12 (). Forecasting with a lead time between 3 hours and several minutes is nowcasting 3 Nowcasting ( ) ).
Nowcasting Nowcasting originates from forecast, where fore (definite time in a future) is replaced by now (the nearest time) (nowcasting) "" (forecast), fore, , now, . Nowcasting term was introduced in 1982 by Browning. It means detail overview of current weather and weather forecast for the next 3 hours using extrapolation method. , 1982 ., 3 .
Object for forecasting Local weather which is observed in a definite place (airport, city district, stadium, skiing route, road) (, - , , , ). Local weather is formed due to influence of mesoscale processes or mesoscale features of synoptic scale objects. . Collection, learning and processing information system for VSRF and Nowcasting requires: Fast information exchange
Error detection and correction Matching of data formats Geographical data binding Providing of high resolution visual images .
Data collection system Satellite data Remote sensing of the atmosphere Meteorological radars network Network of the surface observation stations Satellite remote systems for measuring and observing of the Earth Purpose Information support of modern society with data on environmental conditions and trends in any changes of the environmental conditions
Global space observation subsystem of hydrometeorological purposes It has been developed on the basis of national space systems with WMO coordination. This system has 2 levels: : The satellites of USA, EU, Japan, India, China in geostationary orbit (GOES-E, GOES-W, METEOSAT, MSG, MTSAT, INSAT, FY-2); , , , , (GOES-E, GOES-W, METEOSAT, MSG,MTSAT, INSAT, FY-2); The system of the USA operational satellites (NOAA) and the EU satellites (EPS/MetOp) in mid-level near-polar solar-synchronous orbits
NOAA EPS/MetOp - Receiving of the Earth images in 12-spectrum channels of VS and IR ranges by VCS station The information from geostationary satellites is used for: : Current and VSRF weather forecast Cloud dynamics observation
Drawing of charts on cloud and surface temperature with 3 km resolution 3 , Evaluation of cloud and aerosol parameters , Evaluation of water vapor and ozone content in the atmosphere
Determination of precipitation zones Russian Federal Space Program 2006-2015
In 2008 to start reconstruction of orbital complex of hydrometeorological purpose - (loworbital) and - (geostationary) 2008 . - ( ) - ( ) These satellites will be equipped by measurement devices, similar to NOAA, MetOp and MSG equipment (It is planned to include so-called radar for all-weather ice patrol)
, NOAA, MetOp MSG ( - N1 ). Roshydromet surface complexes of receiving, processing and distribution of satellite information The basis of such system are the three federal centers: : In Moscow region (Obninsk-Moscow-Dolgoprudny), ( - ), In West-Siberia region (Novosibirsk)
- (), In Far-Eastern region (Khabarovsk) (). The complex includes more than 60 independent satellite data receiving stations, which receive information with low spatial resolution 60 () , . Roshydromet surface complexes of receiving, processing and distribution of satellite information The main goals of the complex: receiving data from satellites
data preprocessing main data processing () delivery of information to user data archiving and cataloging Automated meteorological radar complexes The main features of -5: -5:
Wavelenght - 3.2 and 10 cm - 3.2 10 Frequency of pulses -1 and 2 mcs -1 2 Width of directional diagram 1 degree
- 1 Radius of observations 300 km - 300 Purpose of is used for automation of meteorological radar 5 to get the information on cloudiness and associated dangerous hazards (showers, thunderstorms, hail, squall) -5 ( , , , ): Such information is important for: :
Airports, automated air-traffic control systems ; Roshydromet Weather Forecasting Departments Meteorological phenomena chart The identification algorithms allow to determine different types of cloudiness and phenomena .
Types of cloudiness: : Middle level (As-Ac) (As-Ac) Stratus-clouds
Hail: with probability 30-70%,70-90%, > 90% ( 30-70%,70-90%, > 90%) Thunderstorm: with probability 30-70%,70-90%, > 90% ( 30-70%,70-90%,>90%) Squall
Chart of top level of echo radar Measurement range 0-20 km : 0-20 Measurement resolution is 250 m - 250
Resolution of images is 1 km - 1 . Chart of squall velocity Outlines of squall zones. The maximal squall velocity (in m/s) is marked by colors. . /. Chart of hazardous weather phenomena Storm warning Work station Is used to provide road services with a radar information: :
Determination of amount of precipitation for definite area at definite time Determination of amount of precipitation for definite area with a lead-time of 2 hours 2 Visibility range forecast in precipitation area with a lead-time of 2 hours 2 Chart of precipitation intensity (mm/h)
Is measured at every time of observation Is represented on each element of square 4*4 km 4*4 Chart of precipitation amount
Calculation of precipitation amount for any interval of time Isolines drawing Print of charts
Forecaster Work Station Calculation of speed and direction of echo radar motion Determination of trends development
Inertial forecast for echo radar field Inertial forecast of starting time of severe weather phenomena and precipitation for definite areas Inertial forecast for local areas Weather forecast for local areas in 200 km distance from with indication pf the starting time of the phenomena
, 200 , . Work Station The working station is used to combine information from automated network of several meteorological radars Size of area of data integrtion is 1000*1000 km2 10001000 .. Number of Meteorological radars is up to 32
- 32 Projection is polar stereography - The size of cell 1*1 - 11 joint chart of meteorological phenomena
Combination of data from different MR Determination of different types of cloudiness and phenomena Types of cloudiness: Middle level (As-Ac) Stratus-clouds Cumulus clouds Precipitations: weak, moderate, strong Showers: weak, moderate, strong
Hail: with probability 30-70%,70-90%, > 90% Thunderstorm: with probability 3070%,70-90%, > 90% Squall : :
(As-Ac) (, , ) (, , ) ( 30-70%,70-90%, > 90%) ( 30-70%,70-90%,>90%) Joint chart of hights Measurement range is 0-20 km
: 0-20 Measurement resolution is 250 m - 250 Resolution of images is 1 km 1. Vertical cross section The vertical cross section along azimuth
The vertical cross section along the definite route Upper-air sounding Automated upper-air stations (AUAS) () Purpose of AUAS AUAS is used to measure vertical profiles of temperature, humidity, wind velocity, wind direction in the atmosphere using radiosonde.
, , . Monitoring stations and radiosondes : Monitoring stations: : microelectronic upper-air radar (-) with active phased array; monitoring stations such as , (, , ( (radiosonde -2-1), -2 (radiosonde -2-2). -1, 1 and , (-. (-) ( ); , ( -2-1), -2 ( -2-2). -1, -1 -. Radiosondes: - 2-1, - 2-2, -95, -3 - : - 2-1, - 2-2, -95, -3 - Upper-air sounding data
Automated information-measuring system IMS , ( for meteorological provision of road traffic - Purposes Automation of processes of road surface control Dangerous weather phenomena warning
Meteorological provision of road services, Forecasting Departments of the Ministry of Emergency Situations , Main tasks Receiving data from road meteostations net, meteorological radars net and forecasting depertment
, Processing, achiving and representation of information (graphs, tables, charts) , (, , ) Dangerous weather phenomena warning Scheme of AIMS Road meteorological station ROSA
Meteorological parameters (temperature, wind velocity, humidity, precipitation, pressure, visibility) (, , , , ,) Surface parameters (conditions, temperature, freezing point, thickness of water level, amount of chemicals) (, , ,
, ) Dangerous weather phenomena warning Krasnodarsky region 7 (+6) ROSA+video, 4(+1) work station . 7(+6) ROSA+, 4(+1) .. IMS Graphs for 24 hours Hoar-frost formation (typical situation) ( )
IMS Input data for forecast Sources: : Network of the road meteorological stations, meteorological centers,
meteorological radar network MR data (chart of weather phenomena) ( ) MR data (amount of precipitation) ( ) Information from Hydrometeorological Centre Charts
General forecasts O Special forecasts C- Automated information system Meteoexpert
Purposes: Combination of meteorological information of different types ; Dangerous weather phenomena forecast using computational methods
; Weather forecast for aviation coded in TAF TAF ; Dangerous weather phenomena monitoring ; Preparation of weather charts (surface/upper level) (/); Preparation and translation of special weather phenomena charts . Basic functions Dangerous weather phenomena forecast using computational methods
Forecast for aviation (TAF) (TAF) Preparation of weather charts Preparation of forecasting charts
Making of upper-air charts Representation of current weather Representation of radar data
Representation of satellite data Dangerous weather phenomena monitoring Used data Regular standard nets: :
Network of meteorological stations Network of upper-air sounding stations Asynchronous data: : Net of meteorological radars
Net of road meteostations Meteorological satellites World forecasting centers
Aerological diagram A Data from Forecasting Centre (GRIB, Reading) (GRIB, ) Data from Forecasting Centre (GRIB, Braknel) (GRIB, ) Charts on data in codes GRIB and T4 GRIB T4 Charts on BUFR code data BUFR Dangerous weather phenomena forecast
Thunderstorm Fog 30 computational methods ( 30 )
Visibility range in fog Squall Hail
Glaze Low cloudiness Wind and temperature
Some examples of dangerous weather phenomena forecast Forecast of radiation fog Numerical weather modeling Numerical model of atmospheric boundary layer
Forecasting profiles of temperature, humidity, wind, turbulence energy, rate of turbulent energy dissipation , , , , Accuracy of forecast 90 % 90%
Thunderstorm forecast Numerical weather modeling Numerical model of convective cloud Scheme of appearance recognition Radar data for tuning Accuracy of forecast 90 %
90% Automated forecast of dangerous weather phenomena Calculation of trends Using of weather data (receiving time step is 30sec ( 30- )
Calculation of trends Observation of front-line motion Storm information (WAREP, SPECI, ) (WAREP, SPECI, )
Meteorological radar data (Meteocell) (Me) Charts of weather phenomena Charts of precipitation intensity
Storm warning Monitoring of dangerous weather phenomena Computation of replacement
Forecast verification Dangerous weather phenomena forecast using computational methods /radar data and current weather is used also/ / / Main products: weather charts, forecasts
: , Charts of special weather phenomena The basis of system for Very Short Range Automated Forecasting is developed Combination of different data types Monitoring of dangerous weather phenomena
Calculation of trends Dangerous weather phenomena forecast using numerical weather prediction An expert estimated probability of dangerous weather phenomena with respect to synoptic situation
Dangerous weather phenomena forecast in automated regime Calculation of forecast success rate .
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