Report on the JCSDA Microwave Sensors Working Group JCSDA Workshop UMBC, May 2009

Layout MWG Membership Introduction/Status of the MWG MWG Charter What does the MWG usually do? – Providing Status of MW Sensors Health – Sharing Early Info/Data about New sensors – Sharing Information about Calibration and Tools – Sharing Information About Approach & Methodology – Providing Technical Support to JCSDA Management Web Site Information Future Direction

MWG Team Members NAMEAFFILIATIONPHONE ADDRESS Sid BoukabaraNOAA/NESDIS/STAR x 195 Steve John DerberNOAA/NWS/NCEP x 7740 Emily LiuNASA/GMAO Zhiquan Kenneth Will JCSDA Microwave Sounder Working Group New members: - Tom Kleespies NOAA/JCSDA - Tsan Mo NOAA/NESDIS The goal is to have representatives from all JCSDA partners as well as experts in microwave sensors assimilation Invitees: - Banghua Yan NOAA/JCSDA - Fuzhong WengNOAA/NESDIS

Introduction/Status The JCSDA Microwave Working Group MWG had its kick-off meeting on Feb 6 th 2009 MWG meets generally on a monthly basis Its current focus is to serve as a focal point for sharing information among partners, about everything related to microwave sensors: –what channels used in assimilation, –what biases are noticed, –potential anomalies, –Etc It also serves as a technical arm to the JCSDA management for planning everything related to Microwave sensors –Identify steps to transition microwave sensors to operational assimilation and potential gaps

Expected Passive Microwave Sensors New Sensors (Operational) –NPP/ATMS –NPOESS/MIS Follow-on sensors (Operational) –Metop-B,C,etc AMSU and MHS –DMSP-F18,19,etc SSMIS –NPOESS/ATMS (C1, C2, etc) Research sensors –GPM –GCOM –Acquarius –SMAP –Windsat –AMSR-E –TRMM/TMI Non-US Sensors –FY3 MWRI, MWTS, MWHS –SMOS –Mega-Tropiques MADRAS

Review of MWG charter

JCSDA Microwave Sounder Working Group Charter Highlights of Objectives Foster collaboration across organizations. Avoid redundant efforts. Coordinate the preparation for NPOESS ATMS and MIS sensors Coordinate data transfer and data reformatting for microwave radiance data across the JCSDA partners Coordinate efforts on bias correction, preprocessing, data selection and Quality Control methods for microwave radiance data Interact with the JCSDA CRTM team and make recommendations on new CRTM developments to benefit the use of microwave radiance observations Review alternative approaches to assimilation of microwave radiance observations, e.g. cloudy and rain affected radiances, land-sensitive measurements, products such as but not limited to, physical retrievals, hydrometeors and derived emissivity Coordinate and assess impact experiments for NOAA and METOP AMSU- (A/B/MHS/HSB) and SSMIS and other non-operational or non-US sensors Maintain an inventory of JCSDA and its partners use of microwave data in terms of (i) sensors used, (ii) number of observations received (iii) number of observations used (iv) preprocessing steps, data selection, bias correction and QC, (v) average impact on skill as defined and measured by the individual partners. Coordinate the validation of the microwave upper atmosphere radiative transfer modeling and assess the impact of the upper atmosphere radiance observations on NWP and data assimilation impact experiments

Providing Routine Status of the Sensors Health This could include: - Sharing/Comparing bias / noise values - Sharing findings of anomalies if any - Etc.

NOAA-18 and Metop-A NEDT high for Metop-A Channel 7 NEDT still above specs for N18 Channel 4 NOAA-18 NEDT CHANNEL 4 Metop-A NEDT CHANNEL 7 This level of noise went back down since then

Sharing Early Information/Data for New Sensors (for example N19) This could include: - Facilitating the early access of data to all partners - Sharing findings in calibration issues - Etc.

N19 Microwave Data Quality: NEDT Ch# N N Channels12345 NeDT(N18) NeDT(N19) AMSU MHS Pretty stable noise instrument

Sharing Information about Calibration and Tools This could include: - Facilitating the early access of data to all partners - Sharing findings in calibration issues - Etc.

DSMP F-17 SSMIS Flight Software Update Successful SSMIS F-17 Flight Software Upgrade (V9) 19 March 2009 Designed to Mitigate the Elevated Noise Levels as a function of the number of scenes used in calibration average NRL Cal/Val team, Aerospace and NOAA SOCC uploaded and monitored multiple calibration averaging schemes and Early Orbit (EO) modes during the 3 days of implementation and verification Noise levels were reduced by a factor of 1.5 to 2. using 8 scenes per scan NRL analysis shows that an even greater noise reduction factors can be attained by optimizing the beam positions of the warm and cold calibration scene positions for each feedhorn and using 16 scenes per scan

DSMP F-17 SSMIS UPP Update SSMIS Unified Pre-Processor Version 2.1 running operationally at FNMOC for both F-16 and F-17 (4 May 2009) Spatially averaged BUFR files distributed to NOAA by FNMOC UPP V2.1 developed by Bill Bell (Met Office/ECMWF) and Steve Swadley (NRL) UPP V2.1 includes: Reflector Emission Corrections, with sensor and channel dependent reflector emissivities Sensor dependent Reflector Temperature model Gain corrections of the warm load solar intrusions Level of Spatial Averaging controlled at the script level Full resolution BUFR files require WMO BUFR code table changes

Sharing Information about NWP Approach and Methodology This could include: - Sharing Info about what is being done at different centers in terms of improving the assimilation methodology - Sharing info about what data are being assimilated (radiances, products, which sensors, etc) - Assessing the forecast skills improvements - Status of the RTM being used and the obtained results - Etc.

Comparison of Assimilation Impact of AMSU and SSMIS LAS Data (Aug. 15 ~ Sept. 30, 2008) A reliable calibration plays a critical role for producing more positive impact on NWP!

Monthly Averaged NRL Adjoint Impact Assessment of AMSU-A, AIRS, IASI and F16 SSMIS UPP Data in NAVDAS-AR Robust Pre-Processing, Channel Selection, Quality Control also play a critical role for producing more positive impact on NWP Forecast Accuracy

Identifying steps to successfully assimilate a microwave sensor (and potential gaps) Part of the MWG charter is also to provide Support to JCSDA Management:

Steps to transition a new sensor to operational data assimilation (1/2) 1- Pre-Launch phase: –Forward model preparation –Covariance matrix preparation for all parameters in the assimilation control vector –*OSSE experiments using Nature run, to get ready for the assimilation of the sensor and assess impact. –Preparation of the tools for preprocessing data, QC/flagging the data. –Assess hardware and software requirements needed and plan accordingly –Assess footprint averaging, thinning methodologies that are appropriate, for the specific sensor. Thinning is understood as spatially and spectrally. –Obtain sample data –Obtain/generate decoding codes for the sample data –Initial estimates of instrument noise from data –Simulation of a full data assimilation exercise on simulated data (sample files) –Generation of a flow of simulated data, based on proxy real data (for example ATMS data based on AMSU data) with identical format as the expected real data –Set up the ingest system, assess potential bottlenecks and fix issues. Goal: simulate as much as possible the expected configuration after launch, before the launch. *The MWG considers that this step should also be included in the design phase of a sensor (considerably before launch), to help optimize the impact of the additional sensor on the forecast skills

Steps to transition a new sensor to operational data assimilation (2/2) 2- Post-Launch Phase: –Monitor telemetry, noise NeDT, stability of gains, hot loads, cold loads, and other major parameters. –Assess quality of measurements (by comparing to simulation based on forecast/analysis fields) (OB-BK) –Assess geo-location quality of measurements –Assess QC/pre-processing tools (rain flag ice flag, convergence metric, etc) –Inter-compare sensors with legacy sensors (N19 vs. N18, F18 vs. F17/16, etc.) –Determine/monitor bias of measurements as well as RTM uncertainties –Test/Adjust footprint matching & thinning methodology –Perform parallel assimilation tests to determine impact on forecast skills (tailored to the type of sensor: an imager is not likely going to impact hugely the 500 hPa anomaly correlation factor.) –Full operational implementation if tests positive. –Continuous improvement/fine tuning of assimilation methodology, QC, bias adjustment, thinning, etc.

JCSDA MWG Web Site There is a JCSDA web site of the MWG, where we store: –Presentations made during the MWG meetings for future references –Summaries and minutes of the monthly meetings –Charter It’s a Twiki-based web site so web site is dynamic and could be modified by authorized users All pages/documents are version-controlled

Microwave Working Group Home Page Login required: Twiki – platform

JCSDA-MWG Future Direction Future directions of the MWG is to –Continue being the focal point of the JCSDA microwave activities: sharing information among JCSDA partners and help shape management decisions –Identify key projects that are of common interest to JCSDA partners (QC, Data handling, Proxy data, Assimilation of rain- and cloud- impacted radiances, surface-sensitive channels, etc) –Identify expertise centers to handle those identified projects –The end result being to generate CRTM-like packages that could be shared among partners, therefore achieving leverage