1 Impact of the NAME04 Soundings on the NCEP data assimilation systems Wesley Ebisuzaki Kingtse Mo, Eric Rogers, Wesley Ebisuzaki, R. Wayne Higgins, Jack.

Slides:



Advertisements
Similar presentations
North American Monsoon Experiment (NAME) Personal Briefing Page Scott C. Handel NOAA/NWS/NCEP/CPC.
Advertisements

Introduction to data assimilation in meteorology Pierre Brousseau, Ludovic Auger ATMO 08,Alghero, september 2008.
How do model errors and localization approaches affects model parameter estimation Juan Ruiz, Takemasa Miyoshi and Masaru Kunii
Gulf Surges, the Diurnal Cycle, and Convective Outflows as Revealed by the NCAR ISSs in NAME Richard H. Johnson, Peter J. Rogers, Paul E. Ciesielski, Brian.
Rapid Refresh and RTMA. RUC: AKA-Rapid Refresh A major issue is how to assimilate and use the rapidly increasing array of off-time or continuous observations.
The 2014 Warn-on-Forecast and High-Impact Weather Workshop
Reason for the failure of the simulation of heavy rainfall during X-BAIU-01 - Importance of a vertical profile of water vapor for numerical simulations.
My Agenda for CFS Diagnostics Ancient Chinese proverb: “ Even a 9-month forecast begins with a single time step.” --Hua-Lu Pan.
Rapid Update Cycle Model William Sachman and Steven Earle ESC452 - Spring 2006.
Validating the moisture predictions of AMPS at McMurdo using ground- based GPS measurements of precipitable water Julien P. Nicolas 1, David H. Bromwich.
Global Forecast System (GFS) Model Previous called the Aviation (AVN) and Medium Range Forecast (MRF) models. Global model and 64 levels Relatively primitive.
CPC’s U.S. Seasonal Drought Outlook & Future Plans April 20, 2010 Brad Pugh, CPC.
Data assimilation of polar orbiting satellites at ECMWF
NOAA P-3 activities during NAME Michael Douglas, NSSL Co-PI’s: Bill Cotton CSU Joe Zehnder, ASU G.V. Rao, SLU.
ATMS 373C.C. Hennon, UNC Asheville Observing the Tropics.
Assessment of the NCEP data assimilation systems during the NAME04 EOP period Marco Carrera, Kingtse Mo, and Wayne Higgins CPC/NCEP/NWS/NOAA.
Plans for a Sounding Data Monitoring and Display Facility at the NAME Operations Center in Tucson Richard Johnson and Paul Ciesielski Department of Atmospheric.
Utilisation of MT-Satellite observations at NCMRWF : Plan & prospects A.K. Bohra, M. Das Gupta, John P. Geogre, R. Ashrit & A.K. Mitra National Centre.
Assimilation of GOES Hourly and Meteosat winds in the NCEP Global Forecast System (GFS) Assimilation of GOES Hourly and Meteosat winds in the NCEP Global.
The Evaluation of a Passive Microwave-Based Satellite Rainfall Estimation Algorithm with an IR-Based Algorithm at Short time Scales Robert Joyce RS Information.
Challenges in Drought Monitoring and Prediction:
The Eta Regional Climate Model: Model Development and Its Sensitivity in NAMAP Experiments to Gulf of California Sea Surface Temperature Treatment Rongqian.
The fear of the LORD is the beginning of wisdom 陳登舜 ATM NCU Group Meeting REFERENCE : Liu., H., J. Anderson, and Y.-H. Kuo, 2012: Improved analyses.
Impact study with observations assimilated over North America and the North Pacific Ocean at MSC Stéphane Laroche and Réal Sarrazin Environment Canada.
Megha Tropiques (GP Retrieval and Applications plan) Vijay K. Agarwal, MOG/SAC Oct , 2005.
Regional Climate Simulations of summer precipitation over the United States and Mexico Kingtse Mo, Jae Schemm, Wayne Higgins, and H. K. Kim.
Reanalysis: When observations meet models
NAME Climate Process and Modeling Team/ Issues for Warm Season Prediction J. Schemm and D. Gutzler CPC/NCEP/NWS/NOAA University of New Mexico The 30th.
Impact of Tropical Easterly Waves during the North American Monsoon (NAM) using a Mesoscale Model Jennifer L. Adams CIMMS/University of Oklahoma Dr. David.
Meteorological Data Analysis Urban, Regional Modeling and Analysis Section Division of Air Resources New York State Department of Environmental Conservation.
Model representation of the diurnal cycle and moist surges along the Gulf of California during NAME Emily J. Becker and Ernesto Hugo Berbery Department.
05/06/2016 Juma Al-Maskari, 1 Tropical Cyclones.
1 Objective Drought Monitoring and Prediction Recent efforts at Climate Prediction Ct. Kingtse Mo & Jinho Yoon Climate Prediction Center.
1 Results from Winter Storm Reconnaissance Program 2008 Yucheng SongIMSG/EMC/NCEP Zoltan TothEMC/NCEP/NWS Sharan MajumdarUniv. of Miami Mark ShirleyNCO/NCEP/NWS.
1 Hyperspectral Infrared Water Vapor Radiance Assimilation James Jung Cooperative Institute for Meteorological Satellite Studies Lars Peter Riishojgaard.
Data assimilation and Forecast activities in support of NAME Data assimilation and Forecast activities in support of NAME The NAME Team at CPC: Kingtse.
Impacts of Improved Error Analysis on the Assimilation of Polar Satellite Passive Microwave Precipitation Estimates into the NCEP Global Data Assimilation.
Satellite-based Land-Atmosphere Coupled Data Assimilation Toshio Koike Earth Observation Data Integration & Fusion Research Initiative (EDITORIA) Department.
Graduate Course: Advanced Remote Sensing Data Analysis and Application A COMPARISON OF LATENT HEAT FLUXES OVER GLOBAL OCEANS FOR FOUR FLUX PRODUCTS Shu-Hsien.
Application of COSMIC refractivity in Improving Tropical Analyses and Forecasts H. Liu, J. Anderson, B. Kuo, C. Snyder, and Y. Chen NCAR IMAGe/COSMIC/MMM.
Evaluation of radiance data assimilation impact on Rapid Refresh forecast skill for retrospective and real-time experiments Haidao Lin Steve Weygandt Stan.
The Infrastructure, Design and Applications of Observing System Simulation Experiments at NASA's Global Modeling and Assimilation Office By Ronald M. Errico.
NAME Enhanced Observation Period 5 th NAME Science Working Group Meeting November 5-7, 2003 NAME Homepage:
The Hyperspectral Environmental Suite (HES) and Advanced Baseline Imager (ABI) will be flown on the next generation of NOAA Geostationary Operational Environmental.
The frequency distribution of daily precipitation over the U.S. Emily J. Becker 1, E. Hugo Berbery 1, and R. Wayne Higgins 2 1: Department of Atmospheric.
Doppler Lidar Winds & Tropical Cyclones Frank D. Marks AOML/Hurricane Research Division 7 February 2007.
NAME Upper-Air Gridded Datasets: Description and Some Preliminary Results Paul E. Ciesielski Richard H. Johnson, Peter J.
Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.
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.
An Overview of Satellite Rainfall Estimation for Flash Flood Monitoring Timothy Love NOAA Climate Prediction Center with USAID- FEWS-NET, MFEWS, AFN Presented.
Assimilation of AIRS SFOV Profiles in the Rapid Refresh Rapid Refresh domain Haidao Lin Ming Hu Steve Weygandt Stan Benjamin Assimilation and Modeling.
Rapid Update Cycle-RUC. RUC A major issue is how to assimilate and use the rapidly increasing array of offtime or continuous observations (not a 00.
Satellite Data Assimilation Activities at CIMSS for FY2003 Robert M. Aune Advanced Satellite Products Team NOAA/NESDIS/ORA/ARAD Cooperative Institute for.
Towards Assimilation of GOES Hourly winds in the NCEP Global Forecast System (GFS) Xiujuan Su, Jaime Daniels, John Derber, Yangrong Lin, Andy Bailey, Wayne.
High impact weather nowcasting and short-range forecasting using advanced IR soundings Jun Li Cooperative Institute for Meteorological.
NAME SWG th Annual NOAA Climate Diagnostics and Prediction Workshop State College, Pennsylvania Oct. 28, 2005.
Land-Surface evolution forced by predicted precipitation corrected by high-frequency radar/satellite assimilation – the RUC Coupled Data Assimilation System.
Synthesis of work on Budget of Water Vapor and Trace gases in Amazonia Transport and Impacts of Moisture, Aerosols and Trace Gases into and out of the.
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.
© Crown copyright Met Office Review topic – Impact of High-Resolution Data Assimilation Bruce Macpherson, Christoph Schraff, Claude Fischer EWGLAM, 2009.
“CMORPH” is a method that creates spatially & temporally complete information using existing precipitation products that are derived from passive microwave.
Numerical Weather Forecast Model (governing equations)
Rapid Update Cycle-RUC
Aircraft weather observations: Impacts for regional NWP models
NWS Forecast Office Assessment of GOES Sounder Atmospheric Instability
NOAA - LAPS Albers, S., 1995: The LAPS wind analysis. Wea. and Forecasting, 10, Albers, S., J. McGinley, D. Birkenheuer, and J. Smart, 1996:
Winter storm forecast at 1-12 h range
Rapid Update Cycle-RUC Rapid Refresh-RR High Resolution Rapid Refresh-HRRR RTMA.
NAME Tier 1 Atmospheric/Ocean Process and Budget Studies
Issues for regional modeling
Presentation transcript:

1 Impact of the NAME04 Soundings on the NCEP data assimilation systems Wesley Ebisuzaki Kingtse Mo, Eric Rogers, Wesley Ebisuzaki, R. Wayne Higgins, Jack Wollen and Marco Carrera Climate Prediction Center NOAA/NWS/NCEP

2 NAME04 Special Upper Air Soundings During the EOP Altair, Yuma did not get onto the GTS; 25% Loreto data did not get in. All other stations had less than 10 reports missing.

3 Data Impact Experiments NAME04 EOP (1 July – 15 August,2004) Experiments accomplished: CDASw, CDASwt, CDASwtmex GDASw; RCDASw, RCDASwt, RCDASwtmex EDASw, EDASwt. EDASwtmex DASw is the operational product

4 Global and regional data assimilation systems GLOBAL: CDAS2 (Climate Data Assimilation System): T62L28 The CMAP precipitation is used to adjust soil moisture, but no direct assimilation of P GDAS (Global Data Assimilation System) :T254T64 model. No P assimilation Regional : EDAS: ( Eta model 3D-Var Data Assimilation system) 12KM 60 layers. It assimilates radar precipitation data over the continental US. RCDAS ( Regional Climate Data Assimilation System) 32Km, 45 layers, It assimilates P.

5 INPUT data Input data In all assimilation systems All systems use rawinsondes, dropsondes, cloud drift winds from Geostationary satellite and aircraft data getting into the buffer. They all use the TOVS-1B radiance data Major differences Both the GDAS and EDAS use more satellite observations than the CDAS2 and RCDAS: SSM/I wind speeds, TRMM TMI precipitation estimates, NOAA-15, NOAA-16 AMSU-A 1b radiances and NOAA-15 –16 and –17 AMSU-B 1b radiances

6 Ratio=  diff**2/var Z500V850CDASw-CDASwt CDASw-CDASwtmex

7 P over the EOP obs CDASw CDAS wt CDAS wtmex Improvement over the SMO, but not over the AZNM area

8 Vertically integrated moisture flux (vector) (qv,contoured) GDAS CDAS w CDAS wt Diff

9 Puerto Penasco 31.18N, W The CDASw really tries, But the coarse resolution Model is not able to take advantage of the soundings

10 Conclusions 1. The impact of the NAME 04 soundings is largely local and is concentrated over the NAME core region; 2. Over the monsoon region, rainfall improves with soundings, but the coarse resolution model Is not able to take advantage of the soundings to improve the moisture fluxes

Differences of the NAME Soundings are local and concentrated over the Tier I area for both RCDAS and EDAS. 2. At upper level, the impact on winds and Circulations are similar for the RCDAS and the EDAS RCDASEDAS

12 [qv] (contoured) and [qu,qv] flux(vector) with soundings 1.GPLLJ: The GPLLJ from RCDASw and RCDASwt, GDAS are similar,while the EDASDwt shows a stronger jet. 2. The GCLLJ from the GDAS, and EDASw and EDASwt are similar, while the RCDAS depicts a very strong jet with a center over the Gulf of California Units:kg m -1 s -1 EDASw EDASwt RCDASw RCDASwt

13 a)Obs soundings [qv] at Puerto Penasco (31.3N, 113.5W) at northern Gulf of California. unit is (g/kg)(m/s) When the soundings are assimilated, the differences between the observations, EDAS and RCDAS are close. When the soundings are taken out, the differences are greater than 60 (k/kg)(m/s)

14 [qu,qv] anomalies for 3 surge events

15 Impact of soundings on the EDAS fcsts CMORPH EDAS day 2-3 EDASw-EDASwt day 2-3 fcsts EDASw-EDASwt 3hr fcsts

16 Conclusions The impact of the NAME04 special soundings on global and regional analyses and short range forecasts is largely local and is concentrated over the Tier 1 area. The impact on analyses differs from system to system. Overall, the soundings will correct some uncertainties in the assimilation system and improve the analyses

17 Conclusions EDAS:The NAME soundings improve the magnitude and location of the GPLLJ and improve rainfall over northeastern Mexico. RCDAS: Soundings improve the GCLLJ Soundings will improve analyses somewhat, but they will not correct all errors in the system.

18 Qfluxes IMPACT on qfluxes for EDAS IMPACT for RCDAS Differences between EDASw and RCDAS w qv qu

19 Experiments w/wt P assimilation Four experiments: RCDASw(P): with P assimilation, with soundings; RCDASwt(P): with P assimilation, without soundings; RCDASw(no P): without P assimilation, with soundings; RCDASwt(no P): without P assimilation and without soundings

20 Precip from RCDAS EOP mean obs RCDASw(P) RCDASw(no P) RCDASwt(no P)

21 Vertical cross section of qv at 30N Impact of sndings w PImpact of sndings wt P Impact of P w sndings Impact of P wt sndings

22 T2m diff RCDASw(P)-RCDASw (no P) Impact due to P assimilation with soundings RCDASwt(P)- RCDASwt(no P) Impact due to P assimilation Without soundings RCDASw(P)-RCDASwt(P); Impact due to soundings

23 Temperature profile between soundings and molts Black (with P with sndings); Green (with P wt sndings) blue (no P w sndings) Red (no P wt sndings) Over N Mexico, The impact of P assimilation Is larger than the NAME04 soundings No P With P Torreon Chihuahua

24 Conclusions The differences between analyses with and without soundings are smaller than the differences between two regional systems. The P assimilation has large impact on analyses, but the impact differs from the soundings

25 Goal: Prediction of summer precipitation over the United States and Mexico Challenges: 1.Truth: Analyses depend on the model, assimilation system and data inputs. The differences can be very large. We have more than one version of the “Truth”, what are we going to do about?

26 Analyses Analyses can only be as good as the model, assimilation system and the input data including precipitation; Impact of soundings on short range forecasts is small. Why? 1. No changes on long waves; 2. Model’s convection has problems; 3. Targeting?

27 P assimilation (Lin et al. 2004) Compare the model P and observed P; Change the latent heating profile, water vapor mixing ratio and cloud water fields  P (assimilation) is close to P obs;  Changes in E and soil moisture and changes in temperature profile.

28 Difference in moisture transport Impact of Soundings w PImpact of Sndings no P P impact w sndingsP impact wt sndings

29 Input data differences among the NCEP data assimilation systems Not usedUsed NOAA-15 –16 and –17 AMSU-B 1b radiance s Not usedUsed NOAA-15, NOAA-16 AMSU-A 1b radiances Not used Used over land GOES precipitable water Precipitation included through variational scheme and model physics Not usedUsedNot used TRMM TMI precipitation estimates Precipitation included through variational scheme and model physics Not usedUsedNot used SSM/I precipitation estimates Not used Used SSM/I precipitable water Not usedUsedNot used Quickscat wind speed and direction Not used Assigns direction from guess Used directly SSM/I wind speeds u,v T, s and q From the COADS u, v, T, Ps, q Surface ship and buoy observations Ps 10m winds,2m q Psu, v, T, Ps, q Surface land observations not usedu, v GOES water vapor cloud top winds u, vu, v, Tu, v GMS, METEOSAT,GOES cloud drift IR and visible winds CommentsRegional CDAS Eta operationalGFSInput Data

30 Vertically Integrated Meridional Moisture Flux (kg/ms) ( ) GCLLJ RROperational EDAS

31 RROperational EDAS Vertical cross section of qv at 30N

32 For the EOP period, 96- hr fcst was performed each day at 0Z, The RSM errors for Z500 AND v850 over the PNA region (150W- 60W,20-50N) is very small, but at 96 hr, the regional differences over the Gulf and the SMO regions are visible RMS errors ICs CDAS wICs CDAS wt 12h 24h 96 h