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1 00/XXXX © Crown copyright Use of radar data in modelling at the Met Office (UK) Bruce Macpherson Mesoscale Assimilation, NWP Met Office EWGLAM / COST-717 Joint Session
2 00/XXXX © Crown copyright Outline Radar data for assimilation into: –atmospheric models –land surface models Radar data for NWP model verification Radar data for model validation
3 00/XXXX © Crown copyright Rainfall Assimilation at the Met Office 1996 - Operational assimilation of 3-hourly rain rates from UK weather radar network into Mesoscale Model 1997 - NWP forecast impact studies on radar data 1997 - test assimilations of lightning data as proxy rainfall 1998 - Assimilation of hourly radar data 2000 - Radar Quality Estimate used in assimilation 2001/2 - French & German radars added to UK composite 2003 - Retuning for ‘New Dynamics’ model version
4 00/XXXX © Crown copyright Radar Data Impacts - objective scores (v radar analyses) Period 1 - significant benefit up to t+6/12 Period 2 - neutral signal
5 00/XXXX © Crown copyright Radar Data Impacts - subjective OPER t+15 NO radar assimilation t+15
6 00/XXXX © Crown copyright Radar Data Impacts: hourly v 3-hourly data 3-hourly radar data Hourly radar data T+3
7 00/XXXX © Crown copyright Radar Data Impacts: hourly v 3-hourly data 3-hourly hourly
8 00/XXXX © Crown copyright Relative importance of radar assimilation for short-period rain forecasts Radar & satellite aircraft surface No of Cases showing benefit sonde Light rain Heavy rain
9 00/XXXX © Crown copyright Radar Coverage and Quality Estimate (Nimrod system)
10 00/XXXX © Crown copyright Mesoscale Model Rainfall Assimilation Data: hourly surface rain rates, pure radar -no gauge/satellite QC & processing (with help from NWP) : –clutter & anaprop removal-- bright band correction – range correction-- orographic enhancement – weekly radar calibration v gauge Monthly Totals Raw radar Corrected radar
11 00/XXXX © Crown copyright Mesoscale Model Rainfall Assimilation (II) Averaging: 5km raw data ==> 15km (model ~12km) Assimilation: latent heat nudging (LHN) –target rainfall analysis R an = (R mod + W R obs ) / (1+W) –R obs interpolated from hourly values to model timestep –assimilation increments ( T) filtered on scale 2 x
12 00/XXXX © Crown copyright Land Surface Assimilation For agricultural and hydrological products, soil moisture input to mesoscale model Current system: “MORECS” –rainfall data ~150 daily gauges over UK –weekly analysis on 40km grid –interpolated variable is % of average annual rainfall New system: “Soil State Diagnosis Model (SSDM)” –rainfall data: Nimrod radar analysis –hourly analysis on 5km grid (same as SSDM)
13 00/XXXX © Crown copyright UK Mes soil moisture Relax to climatology Radar artefacts MORECS Nimrod SSDM
14 00/XXXX © Crown copyright Radar wind assimilation VAD profile availability (CWINDE project) operational since 2001 monthly monitoring quarterly quality reports
15 00/XXXX © Crown copyright Radar data to verify NWP rainfall forecasts on monthly timescale, gauges and ‘corrected’ radar tell roughly same overall story Model skill Month ==> v gauge v radar
16 00/XXXX © Crown copyright Global Model precipitation verification over U.K Models 6 models, mostly 09/2000-10/2002 12Z runs up to 72h 24h accumulation up-/down-scaled to 96*96 km 2 by box averaging Observations corrected radar data upscaled to 96*96 km 2 by box averaging
17 00/XXXX © Crown copyright Global Model precipitation verification over U.K., 0-24h forecasts Frequency bias
18 00/XXXX © Crown copyright LAM verification v UK radar UK Mesoscale DWD LM Hirlam reference …any more? Work in progress for:
19 00/XXXX © Crown copyright For short NWP trials and batches of case studies, radar provides –better spatial sampling than gauges –clearer link with forecaster’s subjective assessment v radar
20 00/XXXX © Crown copyright …..radar may allow study of model behaviour with higher resolution in time Mesoscale Model trial verification Frequency bias for hourly totals > 0.17mm (v Nimrod radar)
21 00/XXXX © Crown copyright and maps of verification v radar can begin to show land/sea differences v radar Rain/no-rain (>0.4 mm/6hrs) Frequency bias t+12-18 hr forecasts autumn 2000
22 00/XXXX © Crown copyright Observations of Evaporation 02 Apr 200011 Dec 1999 94GHz Radar-Derived Ice Water Content (below 0ºC) Radar data provided by Robin Hogan (Reading Univ.) and RCRU (RAL)
23 00/XXXX © Crown copyright Model/Obs Comparison Average ice evaporative depth scale from the Chilbolton 94 GHz cloud radar and the operational UM for 20 separate days in Oct, Nov, Dec 1999.
24 00/XXXX © Crown copyright FASTEX IOP 16: Validation Comparison of the reference and modified model ice evaporative depth scales with 94GHz radar observation statistics Average depth scale Reference:1260 m Modified: 780 m Obs: 640 m ( 160m) Modified model = higher vertical resolution, double ice evaporation rate and two thirds of ice fall speed
25 00/XXXX © Crown copyright Impact of including rain advection on rainfall distribution. Rainfall rate (mm/hr)Orography (m) Rainfall rate difference (advection-no advection) 10hr model forecast.
26 00/XXXX © Crown copyright Verification Dartmoor River Catchment Rainfall 3 Hour Accumulations Avon & Erme Dart Teign Tamar Exe With Rain AdvectionNo Rain Advection Radar 2km Model Forecasts
27 00/XXXX © Crown copyright Verification Correlation between model surface rainfall and NIMROD radar-derived surface rainfall for Dartmoor With Rain AdvectionNo Rain Advection
28 00/XXXX © Crown copyright Future Plans Limited area 4D-Var operational 2005 –with radar derived surface rainfall data in 2006 Experimental 4D-Var assimilation of radar radial winds –project with Salford University Partial Dopplerisation of UK radar network –7 radars by 2006 Convective scale model by 2008 –develop radar verification techniques –establish viable radar assimilation method
29 00/XXXX © Crown copyright Questions?
1 On the use of radar data to verify mesoscale model precipitation forecasts Martin Goeber and Sean Milton Model Diagnostics and Validation group Numerical.
Page 1 Developments in regional DA Oct 2007 © Crown copyright 2007 Mark Naylor, Bruce Macpherson, Richard Renshaw, Gareth Dow Data Assimilation and Ensembles,
Global vs mesoscale ATOVS assimilation at the Met Office Global Large obs error (4 K) NESDIS 1B radiances NOAA-15 & 16 HIRS and AMSU thinned to 154 km.
Page 1 NAE 4DVAR Mar 2006 © Crown copyright 2006 Bruce Macpherson, Marek Wlasak, Mark Naylor, Richard Renshaw Data Assimilation, NWP Assimilation developments.
1 12/09/2002 © Crown copyright Modelling the high resolution structure of frontal rainbands Talk Outline Resolution dependence of extra-tropical cyclone.
Page 1© Crown copyright 2005 DEVELOPMENT OF 1- 4KM RESOLUTION DATA ASSIMILATION FOR NOWCASTING AT THE MET OFFICE Sue Ballard, September 2005 Z. Li, M.
Trials of a 1km Version of the Unified Model for Short Range Forecasting of Convective Events Humphrey Lean, Susan Ballard, Peter Clark, Mark Dixon, Zhihong.
© Crown copyright Met Office Review topic – Impact of High-Resolution Data Assimilation Bruce Macpherson, Christoph Schraff, Claude Fischer EWGLAM, 2009.
Page 1© Crown copyright 2005 Met Office Verification -status Clive Wilson, Presented by Mike Bush at EWGLAM Meeting October 8- 11, 2007.
Page 1 NAE 4DVAR Oct 2006 © Crown copyright 2006 Mark Naylor Data Assimilation, NWP NAE 4D-Var – Testing and Issues EWGLAM/SRNWP meeting Zurich 9 th -12.
© Crown copyright Met Office Benefit of high resolution data assimilation and observing systems in the Met Office UK NWP model G.T. Dow and B. Macpherson.
The NOAA Hydrology Program and its requirements for GOES-R Pedro J. Restrepo Senior Scientist Office of Hydrologic Development NOAA’s National Weather.
Activity of SMHI (Swedish Meteorological and Hydrological Institute) Presentation for CARPE DIEM kick-off meeting, DLR-GERMANY, January Contact.
COST 717 USE OF RADAR OBSERVATIONS IN HYDROLOGICAL AND NWP MODELS The main objective of the Action is the assessment, demonstration and documentation of.
CARPE DIEM Centre for Water Resources Research NUID-UCD Contribution to Area-3 Dusseldorf meeting 26th to 28th May 2003.
OSEs with HIRLAM and HARMONIE for EUCOS Nils Gustafsson, SMHI Sigurdur Thorsteinsson, IMO John de Vries, KNMI Roger Randriamampianina, met.no.
Page 1© Crown copyright 2004 Meteorological Inputs Groundwater Workshop, Birmingham Murray Dale, 4/11/04.
The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology Nowcasting and Short Range NWP at the.
NWP Activities at INM José A. García-Moya SMNT – INM 27th EWGLAM & 12th SRNWP Meetings Ljubljana, October 2005.
Page 1© Crown copyright 2004 The use of an intensity-scale technique for assessing operational mesoscale precipitation forecasts Marion Mittermaier and.
Joint SRNWP/COST-717 WG-3 session, Lisbon Stefan Klink Data Assimilation Section Early results with rainfall assimilation.
© Crown copyright Met Office Plans for Met Office contribution to SMOS+STORM Evolution James Cotton & Pete Francis, Satellite Applications, Met Office,
Page 1© Crown copyright 2006 NWP in the Met Office.
Page 1© Crown copyright Scale selective verification of precipitation forecasts Nigel Roberts and Humphrey Lean.
From Rain into Water Peter Ewins Chief Executive Met Office.
Modern Era Retrospective-analysis for Research and Applications: Introduction to NASA’s Modern Era Retrospective-analysis for Research and Applications:
Validation of Satellite Precipitation Estimates for Weather and Hydrological Applications Beth Ebert BMRC, Melbourne, Australia 3 rd IPWG Workshop / 3.
Page 1© Crown copyright 2004 SRNWP Lead Centre Report on Data Assimilation 2005 for EWGLAM/SRNWP Annual Meeting October 2005, Ljubljana, Slovenia.
VERIFICATION OF A DOWNSCALING SEQUENCE APPLIED TO MEDIUM RANGE METEOROLOGICAL PREDICTIONS FOR GLOBAL FLOOD PREDICTION Nathalie Voisin, Andy W. Wood and.
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss The Latent Heat Nudging Scheme of COSMO EWGLAM/SRNWP Meeting,
NWP Activities at INM Bartolomé Orfila Estrada Area de Modelización - INM 28th EWGLAM & 13th SRNWP Meetings Zürich, October 2005.
Robin Hogan Ewan OConnor Cloudnet level 3 products.
© Crown copyright Met Office Impact experiments using the Met Office global and regional model Presented by Richard Dumelow to the WMO workshop, Geneva,
DYnamical and Microphysical Evolution of Convective Storms Thorwald Stein, Robin Hogan, John Nicol DYMECS.
Eidgenössisches Departement des Innern EDI Bundesamt für Meteorologie und Klimatologie MeteoSchweiz Institut für Physik der Atmosphäre On the Value of.
Univ of AZ WRF Model Verification. Method NCEP Stage IV data used for precipitation verification – Stage IV is composite of rain fall observations and.
ESA DA Projects Progress Meeting 2University of Reading Advanced Data Assimilation Methods WP2.1 Perform (ensemble) experiments to quantify model errors.
© Crown copyright Met Office EURO4M Work Package 2 (chiefly WP2.1) Richard Renshaw EURO4M GA1, De Bilt, April 14 th 2010.
The Impact of Data Assimilation on a Mesoscale Model of the New Zealand Region (NZLAM-VAR) P. Andrews, H. Oliver, M. Uddstrom, A. Korpela X. Zheng and.
GRAPES-Based Nowcasting: System design and Progress Jishan Xue, Hongya Liu and Hu Zhijing Chinese Academy of Meteorological Sciences Toulouse Sept 2005.
“New tools for the evaluation of convective scale ensemble systems” Seonaid Dey Supervisors: Bob Plant, Nigel Roberts and Stefano Migliorini.
Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss.
Outline Background Highlights of NCAR’s R&D efforts A proposed 5-year plan for CWB Final remarks.
Current status of AMSR-E data utilization in JMA/NWP Masahiro KAZUMORI Numerical Prediction Division Japan Meteorological Agency July 2008 Joint.
Calibration of GOES-R ABI cloud products and TRMM/GPM observations to ground- based radar rainfall estimates for the MRMS system – Status and future plans.
CARPE DIEM 4 th meeting Critical Assessment of available Radar Precipitation Estimation techniques and Development of Innovative approaches for Environmental.
Fine-scale comparisons of satellite estimates Chris Kidd School of Geography, Earth and Environmental Sciences University of Birmingham.
TRMM Tropical Rainfall Measurement (Mission). Why TRMM? n Tropical Rainfall Measuring Mission (TRMM) is a joint US-Japan study initiated in 1997 to study.
Introducing the Lokal-Modell LME at the German Weather Service Jan-Peter Schulz Deutscher Wetterdienst 27 th EWGLAM and 12 th SRNWP Meeting 2005.
Soil moisture generation at ECMWF Gisela Seuffert and Pedro Viterbo European Centre for Medium Range Weather Forecasts ELDAS Interim Data Co-ordination.
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