When Weather Matters Science and Services to Meet Critical Societal Needs (2010) / Chapter Skim
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3 Established Weather Research and Transitional Needs
3 Established Weather Research and Transitional Needs
Pages 49-88
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UNDERSTANDING PREDICTABILITy AND GLOBAL NONHyDROSTATIC COUPLED MODELING the Unted states contnues to mantan world leadershp n weather and clmate research as ndcated, for example, by the worldwde use of weather1 and clmate2 research models developed n the Unted states and the leadershp postons held by U.s. scentsts n nternatonal programs.
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50 50 WHen WeAtHeR MAtteRs these accomplshments, the Unted states s not the world leader n global numercal weather predcton (nWP)
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, and Canadian Meteorological Centre [CMC] model)
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It provides near-uniform coverage over the globe while allowing recursive refinement of grid spacing. Incompatible lateral boundary conditions: Lateral boundary conditions refer to the conditions at the horizontal boundaries of regional atmospheric–ocean–land models that are necessary for running these models and are provided from the output of global models or reanalyses.
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Unified Modeling Frameworks and Coupled Modeling Many global weather forecastng models, such as those at nceP and ecMWF, are hydrostatc because ther grd spacngs are generally greater than 10 km or so. In contrast, many regonal weather research and forecast
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54 54 WHen WeAtHeR MAtteRs models (e.g., WRF) are nonhydrostatc.
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estABLIsHeD WeAtHeR ReseARcH AnD tRAnsItIonAL neeDs 55 tly resolved (avodng the lmtatons of cumulus parameterzatons) n global models; assmlaton of convectve-scale observatons usng advanced methods that elmnate precptaton spn-up and mprove ntal condtons n general; mprovements n cloud mcrophyscs, physcs of the planetary boundary layer (PBL)
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56 56 WHen WeAtHeR MAtteRs dedcated to the operatonal and research communtes for the hgh-resolu ton weather modelng enterprse s requred (see Appendx B)
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estABLIsHeD WeAtHeR ReseARcH AnD tRAnsItIonAL neeDs 57 ocean, or land, progress s lackng wth the fully coupled system. there s also a lack of coherent observatons across the atmosphere–ocean–land nterface for data assmlaton n the fully coupled models.
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; and usng standard cost-benefit analyses to determne the optmal deployment. Recommendation: Global nonhydrostatic, coupled atmosphere–ocean– land models should be developed to meet the increasing demands for improved weather forecasts with extended timescales from hours to weeks.
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estABLIsHeD WeAtHeR ReseARcH AnD tRAnsItIonAL neeDs 59 land observatons, as well as sgnficantly ncreased computatonal re sources to support the development and mplementaton of advanced data assmlaton systems such as 4DVar, enKF, and hybrd 4DVar–enKF approaches. Predictability Intrnsc predctablty of the atmosphere–ocean–land system s a fundamental research ssue.
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. QUANTITATIVE PRECIPITATION ESTIMATION AND FORECASTING The Challenge QPFs (Box 3.1)
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The research commu nity, in turn, could use those ensemble outputs to better analyze, understand, and improve the poor forecast cases, address research questions related to weather forecasting, and also use the operational models in their research. The TIGGE project (THORPEX Interactive Grand Global Ensemble; Bougeault et al., 2010)
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for the north Pacfic ocean. QPF Progress and Seasonal Verification Performance the skll n quanttatve precptaton forecasts, whle laggng progress n forecastng other varables, has ncreased steadly over the past 30 years as measured by equtable threat scores10 (e.g., Fgure 3.2)
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SOURCE: NOAA NWS NCEP Hydrometeorological Prediction Center. Available at http://www.hpc.
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inches of precipitation at 24 hours. SOURCE: NOAA NWS NCEP Hydrometeorological Prediction Center.
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estABLIsHeD WeAtHeR ReseARcH AnD tRAnsItIonAL neeDs 65 trbutes to the skll assocated wth all non-summer seasons (Fgure 3.3)
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Improving the Skill of Precipitation Predictions General crculaton models that have been used for operatonal weather predcton have not represented convectve precptaton systems explctly. that s to say, convectve clouds and storms are smaller than the computa
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estABLIsHeD WeAtHeR ReseARcH AnD tRAnsItIonAL neeDs 67 tonal grd scale and therefore can be mplct by means of a parameterzaton (a swtch of sorts) that redstrbutes heat, water substance, and momentum n the model as f convecton had occurred n approprate places and at the approprate tmes.
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68 68 WHen WeAtHeR MAtteRs layer depth and hgh-resoluton vertcal profiles of wnd and water vapor are necessary, together wth surface analyses that are skllful n capturng mesoscale varablty on a scale of approxmately 10 km or less. In add ton, t s necessary to characterze land surface condtons so as to properly model the correspondng fluxes of latent and sensble heat fluxes as well as the sol heat flux; the latter beng hghly dependent on the vertcal profiles of sol mosture and sol temperature.
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Recommendation: To improve the skill of quantitative precipitation forecasts, the forecast process of the National Weather Service should explicitly represent deep convection in all weather forecast mod els and employ increasingly sophisticated probabilistic prediction techniques. Global and regonal weather forecast models should represent or ganzed deep convecton explctly to the maxmum extent possble, even at resolutons somewhat coarser than 5 km, as may be necessary ntally, n the case of global models.
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70 70 WHen WeAtHeR MAtteRs verficaton of probablstc, hgh-resoluton ensemble model forecasts, eventually supplantng equtable threat scores. HyDROLOGIC PREDICTION Despte the fact that the skll of precptaton forecasts has sgnficantly mproved over the past decade -- especally for cool-season events, as ds cussed n the precedng secton -- the skll n hydrologc forecasts has not kept pace.
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Hydrologic Prediction and a Changing Climate changes n precptaton extremes (storm amounts, frequency, and duraton) are already posng unque challenges n water management at the local to regonal scales.
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72 72 WHen WeAtHeR MAtteRs of real-tme observatons from multple sensors, and are a clear necessty for hydrologc predcton n the future. Unfortunately, the physcs of water cycle dynamcs are not fully understood and physcally based predctve models are not yet avalable, ether beyond sngle nvestgator research or n operatonal practce.
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Simulations with deterministic models will always produce the same answer if they have the same input data, whereas stochastic models result in a distribution of results or a result with an accompanying variance. Deterministic models can also be used in a stochastic way to generate a distribution or ensemble of results from many simulations with a distribution of input parameters (the so-called Monte Carlo method)
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Development of a Community Hydrologic Modeling System In the meteorologcal and atmospherc scences communty, advances n nWP and clmate modelng have been enabled to a large degree by the de velopment of communty-supported models.13 such models (e.g., the WRF and the communty clmate system Model) have provded a framework to test alternatve hypotheses and new parameterzaton schemes, to gude the collecton of new observatons, to prortze scence nvestments based on model performance, to support varous socetal and busness applcatons, and to track the progress of research n a systematc way that allows movng ahead n a robust and traceable fashon.
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Transitioning Research Developments into Operations the nWs has the prmary responsblty among the federal agences to provde advanced alerts -- flood warnngs and forecasts -- n the Unted states. the current operatonal hydrologc forecastng process at the nWs reles on a combnaton of observatons (ncludng precptaton, sol mosture, evapotranspraton, sol type, and land cover)
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76 76 WHen WeAtHeR MAtteRs calbraton usng hstorcal observatons. Recent efforts have extended ths model to a grdded dstrbuted verson, called the Dstrbuted Hydrologc Model,14 whch s used expermentally n some of the RFcs.
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estABLIsHeD WeAtHeR ReseARcH AnD tRAnsItIonAL neeDs 77 drologc Modelng Platform and has ntated an ongong communty-wde dalogue (Famglett et al., 2008)
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Building on lessons learned, a community-based coupled atmospheric–hydrologic modeling framework should be sup ported to accelerate fundamental understanding of water cycle dy namics; deliver accurate predictions of floods, droughts, and water availability at local and regional scales; and provide a much needed benchmark for measuring progress. to successfully translate the nvestment n mproved weather and clmate forecasts nto mproved hydrologc forecasts at local and re gonal scales, and meet the pressng socetal, economc, and envron 19 the overarchng scence queston of the WAteRs network s: "How can we protect eco systems and better manage and predct water avalablty and qualty for future generatons, gven changes to the water cycle caused by human actvtes and clmate trends?
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ths hgh-prorty need was the focus of a recent BAsc report, Observing Weather and Climate from the Ground Up: A Nationwide Network of Networks (nRc, 2009b) , and many of ts authors were partcpants n the 2009 BAsc summer study workshop.
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. Challenge: Why Are Enhanced Mesoscale Observations Needed?
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estABLIsHeD WeAtHeR ReseARcH AnD tRAnsItIonAL neeDs 81 spherc electrcty, bogeochemstry, ecosystems, bogenc emssons, and urban-scale processes; • NWS-related R2O activities to enable very short- and short-range predctons that employ advanced nowcastng technques n the 0- to 3hour range; to mprove analyses of ntal and boundary condtons and short-range predctons for mesoscale model forecasts n the 12- to 48hour range; to enable the mergng of probablstc gudance assocated wth nowcastng and dynamcal predctons n the 3- to 12-hour range; to provde a bass for object-orented verficaton of probablstc forecasts resultng from ensemble technques; and to facltate technque development for advanced applcatons of mesoscale observatons to locally dsruptve weather such as fog, surface cng, thunderstorm ntaton and moton, assmlaton of precptaton measurements, condtons near hurrcane landfall, other hazardous lake and coastal ocean condtons, hazardous urban condtons, fire weather predctons, and hydrologc predctons and warnngs such as seasonal floodng from man stem rvers and flash floods; • Directly serving the missions of numerous federal, state, and local agencies ncludng noAA, the Department of transportaton, DoD, the envronmental Protecton Agency, Doe, UsGs, the Department of Agrculture, nsF, the Department of Homeland securty, and nAsA at the federal level; and several agences n all 50 states, ncludng transportaton, emergency management, water resources, and ar qualty applcatons; • Directly serving and improving productivity in commercial sectors such as renewable (dscussed n detal n chapter 4) and conventonal energy producton ndustres; agrcultural cooperatves and supplers; the commercal ar, sea, and land transportaton ndustres; weather and clmate nformaton corporatons; broadcast meda; commodtes exchange; nsurance/rensurance ndustres; among many others.
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Research and Development Topics Dependent on Mesoscale Observations Mesoscale Data Assimilation there s a pressng need for research and development leadng to mproved mesoscale data assmlaton technques n operatonal forecast systems. Improved analyses requre better knowledge of error covarances n observatons.
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estABLIsHeD WeAtHeR ReseARcH AnD tRAnsItIonAL neeDs 83 are relatvely unnformatve at the mesoscale. Unlke threat scores, object- orented approaches (e.g., Davs et al., 2006a, b)
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84 84 WHen WeAtHeR MAtteRs exploraton. ths ncludes the dynamcs among varous surface-based ob servng networks as well as the exploraton of optmal and cost-effectve dvsons of responsblty between observatons from space and those from arborne and surface platforms.
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estABLIsHeD WeAtHeR ReseARcH AnD tRAnsItIonAL neeDs 85 performance by hgh-resoluton nWP models and chemcal weather predcton at the mesoscale. through advanced data assmlaton technques, t s estmated (nRc, 2009b)
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86 86 WHen WeAtHeR MAtteRs mosture and temperature, t s small enough to represent seasonal varatons and regonal gradents, thereby supportng numerous mportant applcatons such as land data assmlaton systems n support of nWP, water resource management, flood control and hydrologc forecastng, and management of forestry, rangeland, cropland, and ecosystems. Network Architecture and Testbeds to serve multple natonal needs, the Unted states needs a system that s a network of networks n an archtectural sense (nRc, 2009b)
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. Recommendation: Federal agencies and their partners should deploy a national network of profiling devices for mesoscale weather and chemical weather prediction purposes.
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88 88 WHen WeAtHeR MAtteRs educatonal needs, often for lmted perods n lmted regons. If hstory s a proper judge, many of the research-motvated sensors and observatons wll evolve to operatonal status, servng exstng socetal needs better and servng future addtonal socetal needs well.
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