Craig Bishop and John Methven Progress and Plans of the Predictability, Dynamics & Ensemble Forecasting (PDEF) WG October 25, 2016 Craig Bishop and John Methven WWRP-SSC9 meeting, Geneva

Contents PDEF foci Highlights of 2016 International Model and Observation error Detection (IMOD) project PDEF’s contribution to IP Action Areas Plans for 2017 Membership Funding

Five Scientific Challenges PDEF is currently focussing on: Stochastic representation of the effect unresolved processes in numerical models (Judith Berner, Mark Rodwell) Construction of ensemble initial conditions (Craig Bishop) Interactions of diabatic processes with meso/synoptic scale dynamics (John Methven, Mark Rodwell) Assessment of multi-model ensembles and calibration techniques (Munehiko Yamaguchi ) Coupled modelling & assimilation (Oscar Alves) The set of challenges will evolve as priorities change.

2016 Highlights 4-6 April, 2016, Oberpfaffenhofen, Germany: NAWDEX preparation workshop (John Methven) 6-8 April 2016, Reading , UK: PDEF/SPARC workshop on blocking (John Methven, Olivia Martius ) 11-15 April 2016, Reading, UK : PDEF/ECMWF Model error uncertainty workshop. (Craig Bishop, Judith Berner, Mark Rodwell) 25-26 April 2016, Exeter, UK: PDEF Working Group meeting (entire working group). Also convened with DAOS working group for a session on coupled data assimilation and ensemble forecasting. 6-8 July 2016, Manchester, UK. Workshop on the role of diabatic processes in weather systems at the Royal Met Soc conference on “High impact weather and climate”. (John Methven) 28 Aug 2016, 6 days. Grindelwald, Switzerland, Swiss Climate Summer School. (Olivia Martius) 5-9 Sep 2016, Reading, UK. YOPP planning meeting. (John Methven) 19 Sep-16 Oct 2016, NAWDEX field campaign (John Methven)

Overarching scientific aim of NAWDEX: to quantify the effects of diabatic processes on disturbances to the jet stream near North America, their influence on downstream propagation across the North Atlantic, and consequences for high-impact weather in Europe. Features related to the meandering tropopause and jet stream (orange is stratospheric air; cyan marks upper tropospheric PV anomalies).

NAWDEX research aircraft observations

NAWDEX radiosonde launch sites Triangles are radar deep profiler sites (for winds) 10 countries contributing plus 160 EUMETNET launches Over 400 additional radiosondes spanning North Atlantic plus similar number in dropsondes during the campaign month

Extratropical transition of Karl NAWDEX IOP4 and IOP5 With help of WWRP-PDEF support, proposal becomes a reality!

Overarching scientific aim of NAWDEX: to quantify the effects of diabatic processes on disturbances to the jet stream near North America, their influence on downstream propagation across the North Atlantic, and consequences for high-impact weather in Europe. With help of WWRP-PDEF support, proposal becomes a reality! Features related to the meandering tropopause and jet stream (orange is stratospheric air; cyan marks upper tropospheric PV anomalies).

Extratropical transition of Karl Divergent outflow advects tropopause and enhances ridge Building. In part, influence of moisture flux and diabatic heating. But by how much? Quinting, Boettcher & Grams, ETH

Extra-tropical transition of Karl

Extra-tropical transition of Karl Andreas Schäfler, DLR

NAWDEX Data Denial Experiments Intercomparison Lead: Martin Weissmann (LMU Munich) System Leads / Contacts: James Doyle (NRL Monterey) Jun Inoue (NIPR Japan) Ron McTaggart-Cowan (CMC) Akira Yamazaki (JAMSTEC)

Objectives and Systems Full-period denial cycles: DWD ICON (possible): LMU Munich ECMWF IFS (likely): LMU Munich/ECMWF CMC GEM (yes): Canadian Meteorological Centre NRL COAMPS (likely): Naval Research Laboratory JAMSTEC AFES (possible): National Institute of Polar Research Partial denial cycles and/or case studies will also be run by all of the above to focus on specific events and scientific questions: General evaluation of sensitivity to added high latitude observations Assessment of differences in predictability associated with warm conveyor belts and their downstream impacts Analysis of the impact of observations in sensitive regions on predictability Most of these institutions can also run with FSOI (forecast sensitivity to observation impact), useful in case study intercomparisons

Model uncertainty workshop output Fascinating presentations and discussions for those who attended Three reports published on workshop web page.

Highlights of Workshop reports Coarse-graining experiments essential Pursue and utilize improved DA methods for the detection of model and observation error Improve the feedback loop between model error detection and stochastic model improvement Consider observational field campaigns that would better enable DA to expose model error and/or observation error. Pursue regime dependent model error detection Efforts should be made to understand how and why the stochastic model error scheme : improves the ensemble changes the bias and event frequency aspects of the model climate.

What are the Action Areas to achieve the following goals? (i) Confidently identify stochastic (and mean) model error and then represent in ensembles (ii) Accurate ensemble representation of initial condition uncertainty in state estimates of variables like cloud, precipitation, aerosol, water vapor, ice, snow, soil moisture, etc (iii) Better initialize coupled model ensembles

International Model and Observation error Detection project (IMOD) Leading research centers commit achieve their NWP “moon landing” of 1- 12 month coupled model simulations at convection permitting resolutions (~2 km) by 20?? These centers store the output at frequent enough intervals to allow coarse graining and OSSE type experiments. Model A then uses simulation from model B as a proxy for the truth to Test strategies for detecting and representing the systematic and stochastic aspects of the model error of a (>10 km resolution model) Test strategies for discovering the true observation error variance. Develop effective methods for assimilating variables with highly non-Gaussian error uncertainties such as cloud, precipitation, ice and humidity Test coupled model DA strategies. Model error representations derived from these studies are used in climate models and forecast models for both at ranges from 2 days to 2000 years. Weather and Climate prediction researchers from both operations and academia involved in IMOD activities using both global and regional models. International funding agencies should enthusiastically fund this project.

AA 1: Address Limitations Promote research into atmospheric error growth using km-scale models so as to inform data assimilation and ensemble methodologies, as well as to support predictability assessments [HIWeather, DAOS, PDEF, NMR, WGNE] Identify observations that can be used to define the key features of the km-scale initial atmospheric state, particularly during the development phase of mesoscale disturbances [HIWeather, PDEF] Improve the observational and algorithmic tools used to identify, ameliorate and/or represent systematic and stochastic elements of model error [NMR, PDEF] Address limitations on polar prediction through the Year of Polar Prediction (YOPP) core phase (mid-2017 to mid-2019) and subsequent consolidation phase [PPP, S2S, DAOS, NMR, PDEF, SERA, WGNE]

AA 2: Uncertainty Develop good practices for maps of forecast probabilities, including the need for data from individual forecast ensemble members [HIWeather, PDEF] Encourage theoretical and observational based research, including the specification of perturbations, aimed at producing ensembles that more accurately represent the distribution of possible true states at analysis and forecast times [PDEF] Conduct workshops on estimating model uncertainty in data assimilation and ensemble forecasts [DAOS, PDEF, WGNE] Address issues of ensemble prediction at km-scale, including the specification of perturbations for variables with highly Non-Gaussian uncertainty distributions [HIWeather, DAOS, NMR, PDEF]

AA 3: Fully Coupled Develop improved (coupled) data assimilation and ensemble forecasting systems accounting for challenges in the polar regions such as sparseness of observational data [PPP, DAOS, PDEF] Organize workshops on coupled data assimilation, which facilitate improvements in the initialization of coupled model ensemble forecasts and provide a tool for exploring the realism of coupled simulations [DAOS, PDEF] Advance research focussed on understanding coupled model dynamics and processes; encourage new methods for diagnosing systematic and stochastic components of coupled model forecast error; facilitate the development of improved methods for representing stochastic components of forecast error within ensemble forecasting systems [PDEF]

AA 4: Applications Many impacts of weather represent weighted spatio-temporal averages of some aspect of the atmospheric state. To obtain reliable probabilistic forecasts of such averages, the spatio-temporal scales of ensemble perturbations must match those of errors. PDEF shares responsibility in developing methods to ensure that the spatio-temporal scales of ensemble perturbations are correct.

AA 5: Verification Help guide strategies for testing probabilistic forecasts of high-impact weather that ensure that “near-miss”, “false-alarm” and “hit” cases are appropriately included in assessments [PDEF] Encourage forecasting centers to evaluate probabilistic forecasts not only of variables that are points in space-time but also with variables that represent differing spatio-temporal averages of points in space-time. [PDEF]

AA 6: Attribution Improve methods for using observations and ensembles of climate forecasts to improve multi-model ensemble climate forecasts [PDEF] Improve model error representations in climate models [PDEF]

AA 7: Integrated Water Cycle Organize and conduct workshops on coupled data assimilation and ensemble initialization [DAOS, PDEF] Hold workshops on improvement of data assimilation and ensemble initialization in km-scale NWP models, with a focus on assimilation of radar data and assimilation techniques capable of dealing with non-Gaussian distributions [DAOS, HIWeather, NMR, PDEF] Promote development of km-scale coupled atmosphere-ocean-land hydrology prediction systems with a primary aim the improvement of flood predictions for urban areas, whether from sea, river or surface water flooding; particular emphasis will be on coupled data assimilation and coupled ensemble prediction [HIWeather, PDEF] Develop ensemble methods with high-resolution prediction models for improving precipitation and tropical cyclone intensity forecasts [WGTMR, PDEF] Advance methods to better initialize and propagate the highly non-Gaussian uncertainty distributions that are associated with many water-related variables in ensemble forecasts [PDEF, HIWeather]

AA 8: New Observations Promote the development of more accurate ensemble methods for representing the distribution of true states given imperfect hydrometeorological observations with non-Gaussian uncertainty distributions [PDEF].

AA 9: Precipitation Processes Advance methods to better initialize and propagate the highly non- Gaussian uncertainty distributions that are associated with aerosols, clouds and precipitation in ensemble forecasts [PDEF, HIWeather]

AA 10: Hydrological Uncertainty Encourage and help guide the introduction of QPE and QPF uncertainty information into the end-to-end hydrological forecasting chain [PDEF, SERA] Promote the development of the reforecast and quality, high-resolution reanalysis data needed to conduct post-processing R&D [JWGFVR, PDEF] Perform inter-comparisons of methods for improving space-time variability estimates, including the Schaake Shuffle, ensemble copula coupling, and hybridizations of the two [PDEF, JWGFVR]

AA 11: Advanced Methods Promote the evaluation of methods for treating model and ensemble uncertainty in ensemble prediction systems; data assimilation provides a direct tool for understanding whether novel model uncertainty methods will improve the short-term fit of forecasts to observations [DAOS, PDEF] Promote work on ensembles for km-scale hazard prediction that address the issues of synoptic scale forcing uncertainty, perturbations of the km-scale initial state produced by data assimilation, and the details of cloud microphysics and turbulent mixing [HIWeather, PDEF] Improve strategies for ensemble initialization of non-Gaussian near-zero, positive definite variables such as cloud, precipitation, water vapour and aerosol [PDEF (DAOS?)]. Encourage research focussed on fundamental aspects of dynamics, the understanding of predictability and the design of ensemble forecasting systems [PDEF] Help NHMSs with limited resources generate ensembles (e.g., lagging or multi- model / multi-physics techniques, neighbourhood techniques) [NMR, PDEF, HIWeather, with CBS/SWFDP as appropriate] Facilitate international collaboration in the sharing of regional deterministic and ensemble model data [NMR, PDEF]

AA 15: Fully Coupled Continue to support TIGGE, to enable and accelerate research worldwide; in light of increasing data volumes, develop policies and methods for distributed data archival/retrieval (PDEF).

AA 16: Fully Coupled Help guide development of low-cost regional ensemble forecasting and data assimilation capabilities for NMHSs of modest means. [PDEF, (DAOS?)]

AA 17: New Observations Encourage aspects of research (into new types of observations) focussed on fundamental aspects of dynamics, the understanding of predictability and the design of ensemble forecasting systems [PDEF]

AA 18: Fully GOS Help identify observational network designs that are well-suited for identifying and quantifying stochastic elements of model error, as part of a focus on better representing stochastic model error in ensemble forecasts [PDEF] Assess the impact of existing and supplementary observations during NAWDEX, mainly through OSEs and OSSEs [PDEF, (DAOS?, HiWeather?)]

Education & Training or Capacity Building Activities 2016 to 2023 Organize workshops, summer schools and conference sessions on aspects of the PDEF focus areas [PDEF]

PDEF Highlights for 2017 Session on stochastic mode error in 2017 WGNE workshop Use of the NAWDEX campaign period for model error studies. Output tendencies (partitioned by process) from the operational ECMWF ensemble during the NAWDEX campaign. PDEF will encourage theoretical studies in which one would attempt to predict the increase in forecast error variance associated with denying special NAWDEX observations. Munehiko Yamaguchi will be championing the PDEF use of multi- model ensembles focus following Yuejian Zhu’s retirement from the committee. Encourage an IMOD project (coarse graining from global convection permitting models) to help address model error questions and issues associated with the assimilation of non- Gaussian variables associated with the Hydrological cycle.

Some PDEF meetings for 2017

Funding PDEF helped make the case to EUMETNET (in April 2016) for additional radiosonde launches from the operational network during the NAWDEX campaign. PDEF areas associated with humidity, clouds, precipitation and aerosols need funding. Many PDEF areas could be more effectively addressed with IMOD

PDEF members Co-chairs Members Ex officio Craig Bishop – NRL, USA John Methven – U Reading, UK  Members Oscar Alves – CAWCR, Australia Judith Berner – NCAR, US Masayuki Kyouda – JMA, Japan Zhiyong Meng – U Peking, China Mark Rodwell – ECMWF Olivia Martius – U Bern, Switzerland Susanne Theis – DWD, Germany Munehiko Yamaguchi – JMA/MRI, Japan Ex officio TIGGE panel chair: Manuel Fuentes – ECMWF

Future Members? Both Richard Swinbank and Yuejian Zhu ended their terms on PDEF leaving 10 PDEF members (John Methven replaced Richard Swinbank as co-chair). We would like to have an additional member or two: Could WWRP sustain the cost?

Concluding Remarks Past and future PDEF WG activities advance science in the PDEF focus areas. The somewhat repetitious manner in which these foci intersect with the IP’s Action Areas makes it feel a bit tedious and redundant to list them. Please consider allowing additional members for PDEF. Could an international activity/project be found that would help fund, unify and focus the IP activities? (eg Hi-res OSSE?)