Carla Cardinali 1 & Tom Hamill 2 (co chairs)

Transcription

1 Carla Cardinali 1 & Tom Hamill 2 (co chairs) 1 ECMWF Data Division 2 NOAA Earth System Research Lab, Physical Sciences Division Bin Wang (Institute Atm. Phys) Chris Velden (U. of Wisconsin) Daryl Kleist (U. of Maryland) Nadia Fourrie (Meteo France) Mark Buehner (Canadian Met. C.) Mikhail Tsyrulnikov (Roshydromet) Saroja Polavarapu (Environ. Canada) Sharan Majumdar (U. of Miami) Stefan Klink (DWD) 1

2 Coupled data assimilation and its impacts on S2S forecasts jointly sponsored workshop Quantifying impacts of improvements in the observing system on sub seasonal forecasts Identifying cases where coupled DA and forecasts would have a strong role at S2S timescales MJO PDEF Development of ensembles and model uncertainty in ensembles attending each other group meetings Prioritization of coupling state components: coupling of ocean and land with the atmosphere WGNE YOPP and PPP: observation strategies for model development, data denial observing system experiments in polar regions, quantifying analysis uncertainties in polar regions, observation based forecast Working Group on Numerical Experimentation verification WGNE DAOS mutual interests: coordination of activities on reanalyses, common observational databases, and coupled data assimilation DAOS, WGNE, and PDEF. A possible jointly supported workshop on stochastic parameterization, possibly supporting the upcoming April 2016 ECMWF workshop on representation of model uncertainties. DAOS and WGNE/Transpose AMIP: WGNE notes that much could be learned from testing of coupled systems in data assimilation mode. PPP OSEs (e.g. Surf. Pressure as Drifter) Observation Based Forecast Verification Advising in the conduct of selected OSSEs, e.g. YOPP optimal deployment of observations Promoting research into polar DA HiWeather Facilitating demonstrations of the impact of novel HRES/4D observing capabilities, e.g. surface data and all phases of precipitation Facilitating the development of new nowcasting techniques, blending in forecast information from rapid update data assimilation and NWP systems Facilitating assessments of model error in DA and EPS (in collaboration with PDEF) Facilitating inter comparison studies of multi scale, coupled DA for selected cases such as FDPs. Promoting the development of tools to assess the sensitivity of hazard forecasts to observational inputs. 2

3 Model&Observation for Arctic Today Analyses (re analysis) for Arctic are limited by observing system and by model deficiencies. Chronic issues include: strong assumption that forecasts and observations have zero mean expected error, i.e., they are unbiased characterization of observation and first guess error covariance errors in near surface air temperatures the treatment of atmospheric moisture including precipitation and clouds analysis resolution and physical processes parameterization Observations Representativeness: the larger the analysis grid box, the larger the representativeness error Data inhomogeneity: observations are distributed non uniformly in time and space: In situ observations are clustered over population dense areas over land (less than one quarter of that available in midlatitudes). Polar orbiting satellite sampling is better near the poles than near the equator but limitations are: for thermal sensors the cold lower troposphere creates ambiguity in distinguishing clear and cloudy sky conditions. passive microwave sensing is useful for detecting the presence of surface ice cover and for estimating atmospheric temperatures. However, the inability to correctly assign a surface emissivity impedes the use of these sensors in much of the Arctic troposphere e.g., cloudy regions due to the challenges associated with accurate characterization of cloud emissivity

4 Observations (cont) Model&Observation for Arctic Today Correlation of errors. Observations may have been assumed to have independent errors, when in fact they do not. If two observations have correlated errors when the reanalysis system has assumed they were uncorrelated, the system will overweight the influence of these observational data Model Poor knowledge of precipitation amount and phase and soil condition Systematic Bias over or under estimates of temperature or even more complicated biases by scene type (e.g., different biases over ocean, land, and ice) Poor background error covariance representation Representation of smaller scales of motion forecasts used in the assimilation are lacking in variability at the smaller scales of motion, and in the absence of dense observational data, the resulting analyses will lack this variability as well Validation Model space verification: Analysis (operational and own one) and existing Re analysis Observation space verification: Radiosondes Processes validation

5 Model&Observation for Arctic: What to do YOPP Planning Workshops at ECMWF in Reading, UK September 2016 In preparation of the YOPP field campaigns 2017 OSEs based on IPY 2007&2009 observational campaigns Arctic observations should be distinguished between assimilated and describing Arctic processes: the latter may be used indirectly in the evaluation of analysis and background fields Assessment of key in situ Arctic observing system components Use of 2 forms of satellite derived non radiance observations of importance in the Arctic: atmospheric motion vector from Moderate and High Resolution Imaging Spectroradiometer (MODIS, AQUA) wind data obtained from active microwave radar sensors scatterometer Use of the best atmospheric remote sensing scheme over ice and snow Refinements to analysis and forecast systems that are most necessary to improve surface analyses Assessment of the atmospheric moisture budget and energy flux using in situ long term energy flux stations Assessment of the background error covariance matrix Use of all available observations to verify the forecast YOPP

6 YOPP After the 2017 YOPP field campaigns Model&Observation for Arctic: What to do OSEs based on the new observations Evaluate the state, utilization, limitations and potential utility of the current Arctic observation network and the relative forecast performance Examine analyses products and forecast models for potential improvement Describe some of the strengths and weaknesses in analyses for potential Arctic related users Give an assessment of current generation Arctic analyses and how well they represent specific physical processes Areas where post YOPP analyses perform sub optimally should be identified Methodological challenges and data challenges should be identified Give indication on the overall benefits of using coupled atmosphere ocean sea ice land Arctic analyses

7 YOPP Recommendations DAOS recommends to perform preliminary OSEs in preparation of the 2017 IOPs to use at best the new observational campaigns With IOP 2017 it should be possible to give some answers on What model and assimilation developments in the research community are ready What are the major gaps of Arctic surface and near surface processes How to remedy gaps in knowledge and develop improved parameterizations to remedy forecast bias Are new observation platforms necessary, and if so, what are the most key observations Better understanding of model uncertainties and definition of the background error covariance matrix DAOS can provide guidance but generally cannot provide working resources Collaboration on the plan of the experiments Possibility to host experts in institutes involved DAOS supervision to perform, evaluate and verify the analysis and forecast performance It is strongly recommended YOPP funds to also be used to support a dedicated position

8 ECMWF/WWRP Workshop: Model Uncertainty April What are the fundamental sources of model error? How can we improve the diagnosis of model error? What are and how do we measure existing approaches to representing model uncertainty? How do we improve the physical basis for model uncertainty schemes? To ensure that DAOS and PDEF work synergistically on these issues A representative from PDEF should be invited to attend future meetings and workshops of the DAOS working group and vice versa

9 International workshop on coupled data assimilation Centre International de Conférences Météo France Toulouse France 18 th 21 st October 2016 methods for coupled systems ( DAM ) Strong coupling versus weak coupling in DA, localization across domains, coupled covariance estimation and modeling, impacts of assimilation update frequency Observations in coupled data assimilation ( OBS ) Coupled observation operators, observation impacts across domains Role of forecast model in coupled data assimilation ( RFM ) Model parameter estimation and model improvement with coupled DA, impacts of model resolution on coupling, managing model error in a coupled DA system Applications and current initiatives ( ACI ) Operational or pre operational coupled DA initiatives at major prediction and modeling centers, OSSE experiments using coupled DA, coupled DA for climate reanalysis

10 International workshop on coupled data assimilation Special thanks to Steve Penny (UMD) and Meteo France staff for a well run workshop Weakly coupled data assimilation (e.g., ocean/atmospheric forecast models coupled, state estimation not) is relatively mature, with improvements shown at several operational centres (next slide) Strongly coupled data assimilation is still a research frontier, but with several groups demonstrating promising results.

11 Preliminary results of ECMWF CERA 20C Tropical Instability Waves (TIW) are westward propagating waves near the equator (intra=seasonal coupled process) CERA 20C weakly coupled ERA 20C atmosphere only CERA 20C represents TIWs thanks to the ocean dynamics atmosphere is responding accordingly (surface wind stress is sensitive to the ocean TIW) ERA20C no TIWs and wind stress signals (forced by monthly SST) high pass filtered SST (colour) and wind stress (contour) October 29, 2014 Courtesy of E. de Boisseson and Patrick Laloyaux

12 International workshop on coupled data assimilation: Observing systems Many current observing systems that would help coupled data assimilation (e.g., snow observations over the US and China) are not making it onto the GTS. More comprehensive data collection a low hanging fruit project Need wider network of flux observations to support model validation, land, ocean, ice surface Co located observations especially helpful (e.g., ocean/atmospheric observations at the same location and time) Given very long (and costly) periods of DA needed to achieve deep ocean spin up, where a denser network of deep ocean observations available, spin up would be faster and computations less expensive

13 International workshop on coupled data assimilation: and modeling Ocean/atmosphere: major challenge is the computational expense of running a highresolution ocean model. Workshop results showed that much benefit can be achieved with focusing on upper layers of ocean. Land/atmosphere: many land surface analyses generated without DA, forcing the land model with analyzed temp and precipitation. This technology is probably nearing the end of its useful lifespan, with new coupled DA technologies showing promise. Ice/atmosphere: extra methodological challenges here, as linear/gaussian assumptions underlying most DA algorithms problematic in presence/absence of ice. General conclusion: the specific methodologies that may be best are still a subject of much research, but there much of the data assimilation (forward operators, data QC, observation databases) could be done with shared software.

14 What should WMO/DAOS do? Advocacy of and support for initiatives like US Joint Center for Satellite Data Assimilation s JEDI (Joint Environmental Data Assimilation Initiative) to develop a more modular DA infrastructure that can be shared across labs, centres and countries. Facilitate development of modern observation databases that can be shared, easily added to add new data, old field program data to GTS, BUFR format Expect white paper / journal article summarizing state of the science and major recommendations stemming from this workshop (Steve Penny to lead)

15 Assimilation methods Reducing Noise in Ensemble Covariances (Mikhail Tsyrulnikov): A new technique on suppressing noise in ensemble covariances, the filtered spectra and covariances produce substantially smaller analysis errors than the errors of the traditional analysis with covariance localization Hierarchical Bayesian EnKF update (Mikhail Tsyrulnikov): The Hierarchical Bayes Ensemble Kalman Filter (HBEF) aims at the optimal Bayesian update of background error covariances using ensemble members as generalized observations. HBEF provides spatial temporal true field, and also allows estimation of true covariances. In numerical experiments, the HBEF significantly outperformed the traditional EnKF as well as a filter based on the variational analysis Scale dependent localization (Mark Buehner): A new approach for scale dependent spatial localization of ensemble background error covariances. The approach is primarily motivated by the requirements of future data assimilation systems for global (or large domain regional) numerical weather prediction that will be capable of resolving the convective scale.. Preliminary results applying this approach to the Canadian global NWP systems shows a small benefit, though larger benefits would be expected in a higher resolution system with a larger range of scales and when assimilating high resolution observations. 15

16 Observations Future tropical observing systems and data assimilation (Sharan Majumdar). Enhancements to the satellite observing network over the tropics include GOES R (October 2016) with Atmospheric Motion Vectors CYGNSS (8 GPS receivers) and COSMIC 2 (Spring GPS Radio Occultation soundings per day) NASA has just committed to funding a new CubeSat constellation entitled TROPICS microwave soundings New in situ observing platforms include the Global Hawk which can be airborne for over 30 hours Additionally New inexpensive small platforms such as the Coyote Unmanned Aircraft in the boundary layer Towards operational ground based remote sensing networks profilers with ceilometer, doppler lidar and microwave radiometer radar reflectivities 16

17 DAOS co chairs have synthesized two proposals for hosting the 2017 DA Symposium. Florianapolis Brazil has been selected and we are preparing the first draft to be circulating in November DAOS is preparing a short report (5 page statement and/or white paper) on usefulness of OSSEs to inform WMO projects and working groups, together with the broader community, on the various potential applications of OSSEs of relevance to WMO activities New members and co chair proposed to WWRP