3
Established Weather Research and Transitional Needs

There are multiple research and research-to-operations (R2O) issues that have been recognized for some time as important and achievable but that have yet to be completed or implemented in practice. The committee refers to these as established needs for weather research and the transition of research results into operations—in contrast to the emerging needs discussed in Chapter 4. Four established priority needs are identified, and all are in various stages of development, but none have yet been resolved despite having been identified as pressing in numerous previous studies (see Table 1.1). They include global nonhydrostatic coupled modeling, quantitative precipitation forecasting, hydrologic prediction, and mesoscale observations. The reader may question why hurricane intensity forecasting is not included here as an established need. The answer lies in the unavoidable reality that virtually all research and transitional needs have both established and emerging aspects, and so the hurricane intensity challenge is embedded within the following section on predictability and coupled modeling and it is also embedded in the following chapter dealing with emerging needs.

UNDERSTANDING PREDICTABILITY AND GLOBAL NONHYDROSTATIC COUPLED MODELING

The United States continues to maintain world leadership in weather and climate research as indicated, for example, by the worldwide use of weather1 and climate2 research models developed in the United States and the leadership positions held by U.S. scientists in international programs. The nation has also made substantial investments in the development of global satellite, in situ, and remote sensing observing systems. In spite of

1

Weather Research and Forecasting (WRF) model; see http://www.wrf-model.org.

2

Community Climate System Model (CCSM); see http://www.ccsm.ucar.edu.



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3 Established Weather Research and Transitional Needs there are multple research and research-to-operatons (R2o) ssues that have been recognzed for some tme as mportant and achevable but that have yet to be completed or mplemented n practce. the commttee refers to these as established needs for weather research and the transton of research results nto operatons—n contrast to the emergng needs dscussed n chapter 4. Four establshed prorty needs are dentfied, and all are n varous stages of development, but none have yet been resolved despte havng been dentfied as pressng n numerous prevous studes (see table 1.1). they nclude global nonhydrostatc coupled modelng, quanttatve precptaton forecastng, hydrologc predcton, and mesoscale observa- tons. the reader may queston why hurrcane ntensty forecastng s not ncluded here as an establshed need. the answer les n the unavodable realty that vrtually all research and transtonal needs have both establshed and emergng aspects, and so the hurrcane ntensty challenge s embedded wthn the followng secton on predctablty and coupled modelng and t s also embedded n the followng chapter dealng wth emergng needs. 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. the naton has also made substantal nvestments n the development of global satellte, n stu, and remote sensng observng systems. In spte of 1 Weather Research and Forecastng (WRF) model; see http://www.wrf-model.org. 2 communty clmate system Model (ccsM); see http://www.ccsm.ucar.edu. 49

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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). Fgure 3.1 ndcates that the Unted states has made steady progress n global weather forecastng performance, but so have other countres. Wthn the Unted states, however, the performance of the noAA/ nWs natonal centers for envronmental Predcton (nceP) Global Fore- cast system (GFs) s superor to the navy operatonal Global Atmospherc Predcton system (noGAPs) operated by the Fleet numercal Meteorology and oceanography center (FnMoc). In partcular, the gap n model perfor- mance between nceP and the european centre for Medum-Range Weather Forecasts (ecMWF) has not narrowed n the past 15 years. the prmary reason s the slow and sometmes neffectve transfer of achevements n the external research communty to operatonal centers n the Unted states (R2o). Another reason s the lack of nvestment and progress n assmlatng observatons n advanced weather predcton models, whch s also related to the slow R2o process n data assmlaton. In addton, nceP’s hgh- performance computng (HPc) capacty, despte recent upgrades, lags be- hnd the capactes of many other major predcton centers around the world.3 the complextes assocated wth usng a hydrostatc global model (GFs) and a varety of regonal (nonhydrostatc and hydrostatc) models4 makes t very challengng to mantan and mprove these predcton models and assocated data assmlaton schemes, partcularly at the underresourced nceP. As a consequence, the Unted states s not fully realzng the potental benefits of ts substantal nvestments n observng systems. Progress and Remaining Needs As the horzontal grd spacng of models contnues to decrease, espe- cally to less than 10 km, hydrostatc models are no longer approprate, and t s essental that global nonhydrostatc nWP models (Box 3.1) be coupled wth ocean and land models. In fact, Japan’s global non–hydrostatc Icosahedral Atmospherc Model (nIcAM), whch runs on the earth smula- tor5 computer, has reached horzontal grd spacng of 3.5 km (satoh et al., 2008), whch results n a spatal resoluton 10 tmes greater (and an areal 3 these and other findngs have been dscussed n the recently completed external revew of nceP, the “2009 communty Revew of natonal centers for envronmental Predcton,” that was managed by the Unversty corporaton for Atmospherc Research (UcAR). the executve summary of the nceP revew s ncluded as Appendx B of ths report. 4 the nceP webste descrbes the models operated by nceP; see http://www.emc.ncep. noaa.gov/. 5 see http://www.jamstec.go.jp/esc/ndex.en.html.

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estABLIsHeD WeAtHeR ReseARcH AnD tRAnsItIonAL neeDs 51 FIGURE 3.1 The United States and other countries have made steady progress in global weather forecasting performance. Time series of seasonal mean anomaly correlations of 5-day forecasts of 500-hPa heights for different forecast models (Global Forecast System [GFS], ECMWF (EC in legend), UK Meteorological Office [UKMO], Fleet Numerical Meteorology and Oceanography Center [FNMOC], the frozen Coordinated Data Analysis System [CDAS], and Canadian Meteorological Centre [CMC] model) from 1985 to 2008. Seasons are 3-month non-overlapping averages, Mar–Apr–May, etc. for the Northern Hemisphere. The green shaded bars at the bottom are differences between ECMWF and GFS performance. SOURCE: NCEP. Available at http://www.emc.ncep.noaa.gov/gmb/STATS/html/seasons.html. resoluton 100 tmes greater) than the 35-km grd spacng of the hydrostatc GFs model at nceP’s envronmental Modelng center (eMc).6 ecMWF has also upgraded ts operatonal forecasts to 16-km grd spacng snce January 2010. now s an optmal tme to nvest n ths area because of many acheve- ments that have been made n the past decade, such as progress n global 6 see http://www.emc.ncep.noaa.gov/gmb/stAts/html/model_changes.html.

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52 52 WHen WeAtHeR MAtteRs BOX 3.1 Modeling Terminology Convective parameterizations: When an atmospheric model’s grid spacing is too coarse relative to the scales of convective clouds, convective processes cannot be resolved by the model and hence are represented in terms of the grid-scale model variables. This is a necessary simplification that cannot be avoided unless the model grid spacing is small enough to explicitly resolve these convective clouds. High-resolution nonhydrostatic atmospheric models: Nonhydrostatic atmospheric models are models in which the hydrostatic approximation (that the vertical pressure gradient and buoyancy force are in equilibrium) is not made, so that the vertical velocity equation (arising from applying Newton’s second law to atmospheric motion) is solved. This allows nonhydrostatic models to be used success- fully for horizontal scales of the order of 100 m. “High resolution” has different meanings for global, regional, and local models and its meaning also changes with time (or with the increase of computing power over time). At present, “high resolution” usually refers to a few kilometers in horizontal grid spac- ing in global models and around 1 km for regional models. Icosahedral grid: This is a geodesic grid formed by arcs of great circles on the spherical Earth. It consists of 20 equilateral triangular faces expanded onto a sphere and further subdivided into smaller triangles. 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. Incompatible condi- tions can arise from differences in the regional and global models in the model physics (e.g., cloud microphysics), dynamics (e.g., atmospheric waves), or configuration (e.g., topography), and can have a significant and negative impact on regional modeling. Predictability and predictive skill: Predictability refers to the extent to which future states of a system may be predicted based on knowledge of current and past states of the system. Because knowledge of the system’s past and current state is generally imperfect (as are the models that utilize this knowledge to produce predictions), predictability is inherently limited. Even with arbitrarily accurate models and observations, there may still be limits to the predictability of a physical system due to chaos. In con- trast, predictive skill refers to the statistical evaluation of the accuracy of predictions based on various formulations (or skill scores). Predictability provides the upper limit in the time for skillful predictions. Quantitative Precipitation Estimation (QPE): QPE refers to the estimation of precipitation amounts or rates based on remote sensing data from radar, satellites, or lightning detection systems, and also estimates from in situ gauges that may or may not provide spatially representative data. Quantitative Precipitation Forecasting (QPF): QPF refers to forecasts of precipitation that are quantitative (e.g., millimeters of rain, centimeters of snow) rather than qualitative (e.g., light rain, flur- ries), indicating the type and amount of precipitation that will fall at a given location during a particular time period. Testbeds: A testbed is a platform for rigorous testing of scientific theories, numerical models or model components, and new technologies. Testbeds in weather forecasting allow for the testing of new ideas in a live environment similar to that in weather forecasting, and hence accelerate the transition from research to operations.

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estABLIsHeD WeAtHeR ReseARcH AnD tRAnsItIonAL neeDs 53 nonhydrostatc research modelng, progress n data assmlaton (ncludng the assmlaton of WsR-88D radar reflectvty and radal velocty data over the con- tnental Unted states), and ncreased hgh-performance computng capacty. A recent achevement s the establshment of varous testbeds (e.g., the multagency, dstrbuted Developmental testbed center; and the vrtual na- tonal oceanc and Atmospherc Admnstraton [noAA] Hydrometeorolog- cal testbed [HMt]), whch wll be helpful for the R2o transton as venues to test new observng and forecast capabltes. the natonal Aeronautcs and space Admnstraton (nAsA)/noAA/Department of Defense (DoD) Jont center for satellte Data Assmlaton (JcsDA) has also been establshed wth the goal of acceleratng the use of global satellte data n operatonal forecastng. However, extramural fundng to support operatonally orented research at these testbeds s very lmted (Mass, 2006). In addton to the requrement for better weather forecastng models and more efficent and effectve data assmlaton methods, there s a press- ng need for basc research to better understand the nherent predctablty of weather phenomena at dfferent temporal and spatal scales, whch s also relevant to socal scentsts whose research and questons often have elements of scale (see chapter 2). there s also a major emergng weather research queston concernng how weather may change n a changng clmate. these and smlar ssues have been rased n varous prevous stud- es (e.g., PDt–1 [emanuel et al., 1995]; PDt–2 [Dabberdt and schlatter, 1996]; PDt–7 [emanuel et al., 1997]; nRc, 1998b), but lttle progress has been acheved. Although the relatonshp between changes n clmate and weather s mportant both scentfically and practcally, t s outsde the scope of the present study and ths report. It s now wdely recognzed that physcal processes at the atmosphere– ocean–land nterface play a sgnficant role n weather forecastng, such as the mpact of atmosphere–wave–ocean couplng on hurrcane forecastng (chen et al., 2007) and land–atmosphere couplng on near-surface ar tem- perature, humdty, turbulent fluxes, convecton ntaton, and precptaton. the role of bologcal (e.g., vegetaton greenness and leaf area ndex) and chemcal (e.g., trace gases, aerosols) processes n weather and ar polluton forecastng has also receved ncreased attenton. 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. of partcular note are hgh-resoluton nonhydrostatc models (those wth grd spacng around 2 km), whch would remove the dependence of the model on convectve parameterzatons, a major barrer for progress n weather forecastng. Global nonhydrostatc nWP models would provde a unfied framework for global and regonal modelng, and consequently help form a more seamless transton between weather and clmate predctons; the mportance of a unfied framework was recently advocated by the World clmate Research Program n ts new strategc plan (WcRP, 2009). the UK Meteorologcal office7 has adopted ths approach wth postve results (Fgure 3.1). such a unfied nonhydrostatc modelng system wth dfferent configuratons (e.g., as a global model wth a unform horzontal resoluton, as a global model wth two-way nterac- tve finer meshes at specfic regons, or as a regonal model) has also been developed n the Unted states (Walko and Avssar, 2008). the nonhydro- statc WRF model has been wdely used (there are thousands of regstered domestc and nternatonal users from publc agences, academa, and the prvate sector) as a regonal model for research and weather forecastng (e.g., nceP); WRF can also be configured as a global model. However, wth a lattude-longtude grd n the global WRF, a polar filter s stll requred, and a better alternatve grd may be the cosahedral grd (see Box 3.1). ths devel- opment of a unfied framework would facltate and ncrease the nteracton among several communtes that have tradtonally been segregated—the weather and clmate communtes, and the regonal and global modelng communtes. A common model framework would also reduce costs through mproved efficences and enhanced collaboratons n the development of varous model physcal parameterzatons. Hgh-resoluton global nonhydrostatc models have the potental to mprove regonal modelng because many regonal weather predcton and data assmlaton problems are essentally global problems; better global models can also reduce the effects of ncompatble lateral boundary cond- tons for the regonal models through the use of consstent model physcs and two-way nestng. the ablty to run both global and regonal models n two-way nested mode wll also create many new research opportuntes (e.g., to study changes n weather n a changng clmate and the potental upscalng effects on global crculatons). A number of key capabltes reman to be developed for coupled nonhydrostatc models; they nclude sufficently hgh spatal and temporal resolutons that enable convecton and hgh-mpact weather to be explc- 7 see www.metoffice.gov.uk/scence/creatng/daysahead/nwp/um.html.

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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), and nterface physcs related to the atmospherc couplng to ocean and land processes; and desgn and development of convectve-scale ensemble predcton and post-processng systems for m- proved and readly nterpretable probablstc forecasts. Varous numercal methods—spectral, finte-dfference, and finte-vol- ume—have been used n global hydrostatc weather forecastng models. For global hgh-resoluton nonhydrostatc models, further evaluaton of these methods and development of new methods are stll needed. new grd cell structures (rather than the tradtonal lattude-longtude grds) are needed, especally for the treatment at the poles (e.g., Walko and Avs- sar, 2008). A promsng combnaton mght be the finte-volume method wth cosahedral grd cells (e.g., Hekes and Randall, 1995). In partcular, a new global weather forecastng model wth cosahedral horzontal grd, sentropc-sgma hybrd vertcal coordnate, and finte-volume horzontal transport (called “FIM”) has been developed at the noAA earth systems Research Laboratory (esRL).8 the hydrostatc verson of FIM s avalable, but the nonhydrostatc verson remans to be developed. observatons are stll nadequate to optmally run and evaluate most hgh-resoluton models and determne forecast sklls at varous scales. (A detaled dscusson of the opportuntes and needs for mesoscale observa- tons s provded n the last secton n ths chapter.) Perhaps even more challengng s the development of sutable and effectve verficaton and evaluaton metrcs and methods for determnng probablstc forecast sklls at dfferent scales. Hgh-resoluton and ensemble forecasts requre hgh performance com- putng (HPc) capablty for model predctons but also for data assmlaton, post-processng, and vsualzaton of the unprecedented large volumes of data. HPc facltes are currently avalable at some Department of energy (Doe) and nAsA centers, as well as at nsF-sponsored centers, whch are usually used for research and clmate smulatons. It would be benefical to have an HPc center that s dedcated to the support of weather forecastng and research n the academc and related research communty to facltate R2o actvtes. HPc facltes are also suboptmal wthn noAA for opera- tonal weather forecastng. A substantal ncrease n computng capacty 8 see http://fim.noaa.gov.

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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). the recent noAA partnershp wth the Doe oak Rdge natonal Laboratory on HPc support (wth a focus on clmate research and predcton) wll help allevate the HPc demand at nceP. Also crucal s development of mproved soft- ware to ncrease the computatonal efficency and scalablty of the forecast models as well as post-processng and vsualzaton, partcularly at petascale (1015) HPc (e.g., nRc, 2008c). Data Assimilation and Observations Data assmlaton as part of the forecast system s also mportant for acqurng and mantanng observng systems that provde the optmal cost- benefit rato to dfferent user groups and ther applcatons. Data denal experments can selectvely wthhold data from one (or more) system(s) and assess the degradaton n forecast skll. Data assmlaton can be used to determne the optmal mx of current and future n stu and remotely sensed measurements, and also for adaptve or targeted observatons (e.g., Langland, 2005). It s also benefical to understand the mpacts of observng systems on model performance and the resultng forecast accuracy (e.g., Gelaro and Zhu, 2009; Raber et al., 2008). Wth agng satelltes n space and nsufficent satelltes n the nAsA and noAA ppelnes to replace or enhance them, observng system smulaton experments can also help sup- port detaled cost-benefit analyses. Besdes the satellte and radosonde data that are wdely used n global operatonal nWP, the assmlaton of data from radar and other sources s also crucal, partcularly for regonal forecastng. Prelmnary work done at the Unversty of oklahoma (Xue et al., 2009) ndcates that a hgh-resoluton regonal model ntalzed usng global model output has relatvely large ntal errors n precptaton forecastng but these errors do not further n- crease wth tme n the first few hours. on the other hand, ther results also ndcate that regonal precptaton forecastng wth radar data assmlaton has smaller ntal errors but they ncrease rapdly wth tme n the first few hours, as expected from our understandng of atmospherc predctablty. For data assmlaton n hgh-resoluton, cloud-resolvng, and coupled ar–sea–land models, t s partcularly mportant to address the nconsstency n model physcs and observatons. For nstance, the cloud droplet sze dstr- buton assumed n models may not be the same as that assumed n satellte- retreved cloud propertes. Although sgnficant progress has been made n data assmlaton usng the ndvdual model component of the atmosphere,

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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. It s mportant to crtcally compare dfferent advanced data assmla- ton methods, (e.g., the ensemble Kalman filter [enKF] and 4-dmensonal varatonal [4DVar] analyss) wth those methods currently used by some operatonal models such as 3-dmensonal varatonal (3DVar) approaches. For nstance, the navy’s FnMoc recently ntated operatonal use of the naval Research Laboratory’s 4DVar data assmlaton system to replace the 3DVar n noGAPs. Prelmnary mpact tests (Xu and Baker, 2009) ndcate that equvalent 5-day forecast skll wth the 4DVar system s extended about 9 hours n the southern Hemsphere and 4 hours n the northern Hemsphere, and tropcal cyclone 5-day track forecast errors are reduced. smlarly, the experments at the noAA esRL (MacDonald, 2009) ndcate that the enKF mproves global weather forecasts compared wth the 3DVar system. A communty consensus s emergng that the future of data assmla- ton may belong to a hybrd enKF-4DVar system (e.g., Zhang et al., 2009); the pace n testng and mplementng such a hybrd system needs to be accelerated at nceP. Model advances—such as mproved grd resoluton, model physcs, and data assmlaton—also mprove the performance of short-range, 0- to 12-hour forecasts. these forecasts are of hgh socetal relevance for many applcatons, such as forecastng severe weather (warnng the publc and protectng lves and property), wnd speed/drecton changes (mprovng the use of wnd-generated power), solar radaton (for solar power generaton), vsblty (for surface and avaton transport), and ar polluton (for publc health). such forecasts also demand and stmulate the development of new observng technologes and measurements, such as the hgh-performance, low-cost, polarmetrc X-band radar networks beng developed wthn the collaboratve Adaptve sensng of the Atmosphere (cAsA) program (McLaughln et al., 2007, 2009). observatonal data wth hgh temporal and spatal resoluton are crucal to the understandng of atmospherc processes, provdng data for assmla- ton n models, and evaluatng and mprovng those models. ths requres the synergstc combnaton of data from dverse sources. Rawnsonde, radar, satellte, and arcraft data as well as data from other sources all play complementary roles n weather research and forecastng. Rawnsonde coverage needs to be mantaned and enhanced because of ts value for weather forecastng and evaluaton of satellte data. Geostaton- ary satelltes provde excellent temporal coverage, but new technologes are

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58 58 WHen WeAtHeR MAtteRs stll requred to add passve mcrowave sensors that can penetrate through clouds. Polar orbtng satelltes provde global coverage wth better spatal resoluton, but actve mcrowave, nfrared, rado frequency, and optcal sen- sors are stll needed to provde data on the three-dmensonal structure of the atmosphere. soundngs of refractve ndex (as a functon of atmospherc temperature and the partal pressures of dry ar and water vapor) from global postonng system (GPs) satelltes have also proved very useful for weather forecastng. neXRAD (WsR 88-D) radars provde excellent precptaton detecton above the PBL over relatvely flat surfaces, but ther spatal cov- erage—especally over the western Unted states—s far from complete, and some radars are not optmally sted for weather research and forecast- ng. the ncrease n the number of radar stes and technologcal upgrades (e.g., the planned dual-polarzaton capablty) are very much needed. the massve numbers of surface networks need to be effectvely used n data assmlaton. smlarly, methods to make good use of the large numbers of automated meteorologcal reports from commercal arcraft need to be developed (Monnger et al., 2003). More detaled dscusson on mesoscale observatons are provded n the last secton of ths chapter. Fnally, a natonal capacty needs to be developed for optmzng the transtonng of envronmental observatons from research to operatons. such a capacty s stll lackng at present (nRc, 2009b). A ratonal five-step procedure for configurng an optmal observng system for weather predc- ton was proposed a decade ago by a prospectus development team, PDt–7 (emanuel et al., 1997) and remans relevant today. Brefly summarzed, the procedure nvolves dentfyng specfic forecast problems; usng contem- porary modelng technques; estmatng the ncremental forecast mprove- ments; estmatng the overall cost (to the naton, rather than to specfic federal agences); 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. these modelng systems should have the capablty for dfferent configuratons: as a global model wth a unform horzontal resoluton; as a global model wth two-way nteractve finer grds over specfic regons; and as a regonal model wth one-way couplng to varous global models. Also requred are mproved atmospherc, oceanc, and

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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 fun- damental research ssue. even though predctablty has been studed for the past half century and was a major theme n the stormscale operatonal and Research Meteorology (stoRM) documents n the 1980s (e.g., ncAR, 1984), not much s known today about the nherent lmts to predctablty of varous weather phenomena at dfferent spatal and temporal scales. Because of the error growth across all scales, from cumulus convecton to mesoscale weather and large-scale crculatons, a hgh-resoluton (preferably cloud-resolvable) nonhydrostatc global model s crucal to address such error growth and better understand the predctablty of weather systems. Although predctablty s obvously mportant to operatonal forecastng, ncreased emphass on basc scence (such as the lmts of predctablty) would be benefical to the greater weather communty. Another fundamental queston of predctablty s error growth across varous scales. ths ssue s partcularly mportant as hgher-resoluton non- hydrostatc global models are developed. For nstance, how up-scalng error growth from convectve scales affects the larger scale crculaton s poorly understood n both regonal and global models. some recent modelng experments ndcate that ncreasng model resoluton may not mprove forecastng skll for the first few days but may mprove forecasts for days 3 through 5 (MacDonald, 2009). Actual predctve skll may lkely be dependent on the specfic phe- nomenon (e.g., mesoscale convectve systems [Mcss] versus tornadoes). It s dfficult to assess predctve skll, because the lack of skll can result from problems arsng from data and data assmlaton deficences, er- rors n numercal representaton, ntrnsc predctablty lmtatons, and forecast verficaton methodology. Retrospectve forecasts have been found to be helpful n better understandng forecast errors and mprovng global forecast sklls (Hamll et al., 2006). to address both ntrnsc predctablty and predctve skll, global nonhydrostatc modelng can be helpful. If such models are used operatonally and f they become user-frendly and avalable to the research communty, researchers wll be able to assst n dagnosng the sources of errors by rerunnng modelng cases wth large (and small)

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78 78 WHen WeAtHeR MAtteRs and envronmental Research systems (WAteRs) network ntatve (nRc, 2009a) proposes a seres of observatores to address the challenges of eco- system sustanablty and water avalablty under changes caused by human actvtes and clmatc trends.19 Unque opportuntes to use remotely sensed observatons from surface and space requre exploraton (e.g., Ylmaz et al., 2005). these nclude cur- rently avalable data (e.g., see the followng secton on observatons) and the new stream of data that wll be delvered by nAsA’s Global Precpta- ton Measurng msson as well as the satellte sol mosture measurements of the sol Mosture Actve-Passve.20 the use of satellte observatons for mproved hydrologc predcton offers ample research challenges and op- portuntes, such as retreval algorthm development over land, multchan- nel ntegraton, bas adjustment, and ncreasng accuracy and resoluton va product blendng. Remotely sensed observatons are the only observatons for many parts of the world and ther optmal use n hydrologc forecastng needs to be fully explored. In the Unted states, the ongong upgrade of the nWs neXRAD radars to dual-polarmetrc capablty wll sgnficantly mprove the ablty to resolve hydrometeorologc varables mportant for the development of coupled atmospherc–hydrologc models. Also, as clearly artculated n nRc (2009b) and dscussed n the followng secton, a ground-based natonal network of networks at the mesoscale, nclusve of rado and optcally sensed PBL profiles of water vapor and wnds s expected to dramatcally mprove our predctve ablty of the coupled atmospherc–hydrologc system. Recommendation: Improving hydrologic forecast skill should be made a national priority. 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?” (WAteRs, 2009). 20 see: http://nasascence.nasa.gov/earth-scence/decadal-surveys/Volz1_sMAP_11-20-07.pdf.

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estABLIsHeD WeAtHeR ReseARcH AnD tRAnsItIonAL neeDs 79 mental demands of water avalablty (floods, droughts, and adequate water supply for people, agrculture, and ecosystems), an accelerated hydrologc research and R2o strategy s needed. Fundamental research s requred on the physcal representaton of water cycle dynamcs from the atmosphere to the subsurface, probablstc predcton and uncer- tanty estmaton, assmlaton of multsensor observatons, and model verficaton over a range of scales. Integral to ths research are ntegrated observatores (from the atmosphere to the land and to the subsurface) across multple scales and hydrologc regmes. contnuous support of ndvdual models geared toward mprovng specfic components of the hydrologc cycle s a necessary element of progress. However, the commttee recommends that a communty organ- zaton around benchmark hydrologc and coupled atmospherc–hydrologc modelng systems s necessary at ths stage to further advance hydrologc predctons, n both research and operatonal modes. such benchmark models wll provde for the synthess of deas and data, avod duplcaton, dentfy major research gaps, provde metrcs of success and track progress, and provde a platform on whch communtes can share knowledge (e.g., there s a need for ecologsts to have access to hydrologc models, and a communty-led framework would be useful n the same way that WRF has been useful to decson makers across wdely dverse sectors). Also, such a communty-based modelng framework wll accelerate the development of a focused R2o and o2R strategy based on a sustaned and effectve academa–ndustry–government partnershp. MESOSCALE OBSERVATIONAL NEEDS Improved observng capabltes at the mesoscale are an explct aspect of every weather prorty dentfied n ths study, ncludng socoeconomc prortes such as reducton n vulnerablty for dense coastal populatons, and mprovements n forecasts at the scale of flash floods and routnely ds- ruptve local weather. the 2009 BAsc summer study workshop partcpants dentfied the underlyng need for enhanced mesoscale observng networks throughout the oral presentatons and n the workng group dscussons. ths hgh-prorty need was the focus of a recent BAsc report, Observing Weather and Climate from the Ground Up: A Nationwide Network of Net- works (nRc, 2009b), and many of ts authors were partcpants n the 2009 BAsc summer study workshop. Accordngly, ths secton draws heavly on the findngs and recommendatons contaned n that report and sup-

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80 80 WHen WeAtHeR MAtteRs ports each of ts recommendatons concernng observng-system techncal requrements. observatons at the mesoscale are an mportant part of the natonal and global observng systems. the global observng system captures large-scale crculaton features and ther related thermodynamc context. components of the global observng system nclude varous satelltes and constellatons thereof; radosondes and regonal electromagnetc profilng devces; and reference surface statons at key locatons that mantan and extend the sur- face clmate record. For example, geostatonary satelltes provde excellent temporal coverage, but new technologes are needed to penetrate clouds for thermodynamc nformaton. observatons based on GPs constellaton occultaton technques contrbute mportant temperature and humdty n- formaton, prncpally at the synoptc scale. observatons at the mesoscale are ncreasngly mportant to establshng ntal condtons for global models as resoluton mproves. Mesoscale components nclude observatons that ether resolve meso- scale atmospherc structure or unquely enable nWP at the mesoscale. the emphass on mesoscale observatons s motvated by the scale and phenom- enology assocated wth dsruptve weather; the necessty to understand t, detect t, and warn of the potental consequences; and mproved capacty to specfically predct or otherwse antcpate t at very short to short ranges (0 to 48 hours). Challenge: Why Are Enhanced Mesoscale Observations Needed? Perhaps the smplest answer to ths queston s that hgh-mpact weather happens at the mesoscale, and because the lowest 2 to 3 km of the at- mosphere are, at once, underobserved and most mportant for processes such as convecton, chemcal transport, and the determnaton of wnter precptaton type. A more complete justficaton for mesoscale observatons resdes n the requrements to serve a wde varety of stakeholders nclusve of ba- sc and appled researchers, ntermedate users assocated wth weather- clmate nformaton provders, and a wde varety of end users at all levels of government and numerous commercal sectors. some examples of these requrements nclude • Basic research n the geoscences and bogeoscences, ncludng studes of mesoscale dynamcs, gravty waves, clmate scence, atmospherc chemstry, mcrometeorology, hydrometeorology, cloud physcs, atmo-

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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 3- hour range; to mprove analyses of ntal and boundary condtons and short-range predctons for mesoscale model forecasts n the 12- to 48- hour 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 re- sultng 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 en- vronmental 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 sec- tors 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; nsur- ance/rensurance ndustres; among many others. Progress in the Past Decade As reported n nRc (2009b), mesoscale surface observatons have pro- lferated enormously n the past decade; so much so that the mesoscale surface observatons enterprse s ubqutous across approxmately 20 federal agences, all 50 states, countless local water and ar qualty dstrcts and au- thortes, numerous Fortune 500 corporatons, countless small- and medum- szed commercal applcatons, the prvate weather nformaton ndustry, and others. However, progress n applcaton of these data s mpeded (nRc, 2009b) by a lack of coheson, coordnaton, and knowledge of standards,

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82 82 WHen WeAtHeR MAtteRs thus lmtng most surface networks to a sngle use status when much of the same data could, n prncple, serve multple natonal needs. Despte the prolferaton of surface statons, there contnues to be a pau- cty of vertcal profile data, whch are badly needed to support montorng of the PBL from 2 m below the land surface to 2 km above; and to enable mproved weather, chemcal weather, and hydrologc predctons. 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. the bass for ths knowledge s weakened by the fact that mesoscale data are often sparse or patchy, and relatvely poorly documented compared to those from standard synoptc observatons. the structure and varablty of the lower troposphere s not well known owng to the fact that vertcal profiles of water vapor, temperature, and wnds are not systemat- cally observed at the mesoscale (schlatter et al., 2005). the senstvty to these observaton gaps s not well understood but s lkely to be substantal n urban (Dabberdt et al., 2000) and coastal (Droegemeer et al., 2000) regons, where populaton densty s hgh, and n mountanous regons, whch are a proxmate cause for major forecast errors (“busts”) downstream (smth et al., 1997). the relatve absence of hgh-resoluton PBL profiles s a vexng problem that greatly mpedes progress n skllful predctons at the mesoscale over both land and coastal waters. Whereas mesoscale weather events can be produced by forecast models from purely synoptc-scale ntal condtons, mesoscale predctablty s also dependent, to some consderable degree, on knowledge of the mesoscale ntal condton. ths s especally true wth respect to specfic predctons of deep most convecton and at- tendant heavy ranfall and severe weather (Frtsch and carbone, 2004). Verification Research and development are needed that lead to mproved forecast verficaton and from whch errors n the forecast system can be quantfied, understood, and rectfied. especally wth respect to verficaton of pre- cptaton, statstcal scores such as equtable threat can be msleadng and

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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) can allow for both ntermt- tency and relatvely small spatal and temporal uncertantes. ths permts the quantficaton of errors and enables a useful characterzaton of skll n predctons wthn regons contanng, for example, ran bands, mesoscale convectve systems, and solated storms. For example, current operatonal verficaton technques are nsufficent, for hydrologc predctons. there exsts consderable senstvty to small- scale varablty n precptaton spatal dstrbuton; the probablty densty functon of precptaton rate; subsequent melt rate n the case of ce-phase precptaton; and surface losses such as evaporaton of lqud water and sublmaton of snow and ce. several of these factors argue for verficaton methods that can better nform the hydrologc user of precptaton estmates and forecast data than an equtable threat score. object-orented methods can nclude specfic locaton, sze, shape, rate, and translaton nformaton n addton to cumulatve amount. Both mproved observatons, such as those from polarmetrc radar, and catchment- and convecton-resolvng models are necessary to acheve ths level of sophstcaton, as well as ths level of verficaton and near-real-tme nput to dstrbuted hydrologc models. Surface and Boundary-Layer Fluxes Research s needed as well that leads to mproved knowledge and repre- sentaton of meteorologcal and chemcal fluxes, emssons, and deposton n hgh-resoluton weather and clmate system models. ths ncludes natural and polluted terrestral boundary layers; marne boundary layers; land– atmosphere exchange dependences on canopy propertes, sol mosture and temperature; urban surface energy exchange and emssons; upper ocean heat content and surface wave propertes; bogenc emssons of volatle organc compounds; gas phase-to-aerosol converson, total aerosol burden, and ts vertcal dstrbuton and transport. several of these fluxes are ether known or suspected to modulate precptaton and the harmful effects of polluton. Observing System Testbeds It s mportant that mesoscale observatons are a focus of testbeds, whch are ntended to develop and ntroduce new deas and new procedures n envronmental observaton. For example, advanced concepts n moble, targeted, adaptve, and collaboratve observng networks requre extensve

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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. Forecast System Testbeds Another focus of testbeds can be to examne the role of mesoscale observatons for new paradgms n the end-to-end forecast process. ths s partcularly mportant and urgent wth respect to mergng methods n nowcastng wth those of dynamcal predcton n the 0- to 6-hour range. ths strategy may be central to mproved performance n severe weather, flash floods, other hydrologc forecasts, and metropoltan area applcatons n routnely dsruptve weather. Mesoscale Observing Needs Atmospheric and Subsurface Profiles these mesoscale observng needs and recommendatons consoldate and follow the general sense of recommendatons of nRc (2009b), where more detaled dscusson and analyss can be found. the hghest prorty observatons needed to address current nadequaces nclude those tems for which there are essentially no systematic national capabilities to resolve or enable mesoscale predcton; these nclude • heght of the PBL, • sol mosture and temperature profiles, • hgh-resoluton vertcal profiles of humdty, and • profiles of ar qualty and related chemcal composton above the surface layer. Improvements are also needed n measurements of drect and dffuse solar radaton, wnd profiles, temperature profiles, surface turbulence, sub- surface temperature profiles, and near-surface cng. Humdty, wnd, and durnal boundary layer structure profiles are the hghest prorty for a natonal mesoscale network, the stes for whch need to have a characterstc spacng of approxmately 150 km but could vary be- tween 50 and 200 km based on regonal consderatons. such observatons, although not fully mesoscale resolvng, are essental to enable mproved

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estABLIsHeD WeAtHeR ReseARcH AnD tRAnsItIonAL neeDs 85 performance by hgh-resoluton nWP models and chemcal weather pre- dcton at the mesoscale. through advanced data assmlaton technques, t s estmated (nRc, 2009b) that data from approxmately 400 stes, when used n combnaton wth advanced geostatonary satellte nfrared and mcrowave soundngs, GPs constellaton “wet delay” and rado occultaton measurements, and commercal arcraft soundngs, wll effectvely fill many of the crtcal gaps n the natonal mesoscale observng system. Among observatons from space, the geostatonary platform component s especally mportant for water vapor and clouds because t preserves the ntegrty of tme-doman samplng wth respect to mesoscale varablty n the lower troposphere. owng to the hgh orbt, vsble and nfrared nstru- ments are preferred n mantanng horzontal resoluton at the expense of obscuraton by clouds. notably, nRc (2009b) recommends both nfrared hyperspectral nstruments as well as synthetc thnned aperture array nstru- ments at cloud-penetratng mcrowave frequences on geostatonary plat- forms. A reasonable performance expectaton s for satellte observatons to assume a prmary role at alttudes above the contnental PBL. the core set of ar qualty and atmospherc composton profiles above the atmospherc surface layer would nclude measurements of carbon mon- oxde (co), sulfur doxde (so2), ozone (o3), and partculate matter less than 2.5 mcrons n sze (PM2.5); these chemcal composton profiles are needed at about 200 stes, whch would yeld a characterstc spacng of approxmately 200 km. these observatons would consttute a natonal pol- lutant consttuent backbone and would be especally effectve n enablng ar qualty (chemcal weather) predcton when collocated wth surface meteorologcal observatons and related vertcal profiles. the selected core chemcal speces have varous mpacts (e.g., on human health), may be harmful to natural and managed landscapes, may serve as precursors to other hazardous compounds, and can help to extend the utlty of param- eters observed from space. Addtonal mportant parameters (e.g., no2) could be added as soon as approprate and when affordable technology s developed for the applcatons envsoned. the proposed network would mprove chemcal weather predcton natonally and also support urban ar polluton montorng, for whch t s not a substtute. sol mosture and temperature measurements are needed to a depth of 2 m at a characterstc spacng of about 50 km, whch corresponds to about 3,000 multple-sample or area-ntegrated stes. these sol measurements are requred, together wth surface atmospherc measurements, to quantfy sur- face fluxes of latent and sensble heat. Although ths spacng s nsufficent to capture the full spectrum of short-term spatal varablty of surface sol

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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). the term “arch- tecture” ncludes the fundamental physcal elements as well as the organza- tonal and nterfacal structure of the mesoscale network. It also descrbes the nternal nterfaces among the system’s components, and the nterface between the system and ts envronment, especally the users. ths archtecture would facltate a thrvng envronment for data provders and users by promotng metadata, standards, and nteroperablty, and enablng access to mesoscale data, analyss tools, and models. the effort would also nclude a process that contnually dentfies crtcal observatonal gaps, new measurement systems and opportuntes, and the evolvng requrements of end users. Appled research and development would nclude but not be lmted to transtonal actvtes, ncludng the operaton of prototype networks and evaluaton of ther forecast mpact (.e., testbeds); development of tools to facltate data access for real-tme assmlaton; development of addtonal tools to serve the general publc and educate the ctzenry; and exploraton of advanced and nnovatve technologes to serve multple natonal needs better, cheaper, and sooner than otherwse mght be possble. testbeds may be operated by natonal laboratores, unverstes, or jont nsttutes as appro- prate to the applcaton. such actvtes are nherently multdscplnary and must be tghtly coordnated f R2o objectves are to be acheved. testbeds may have a sharply focused, lmted term of actvty that fully ntegrates users n the transton to operatons. collaboratve and adaptve sensng and related technologes can effi- cently enhance the detecton and montorng of adverse weather for hazard mtgaton and other applcatons, partcularly for convectve scales and n complex terran, coastal, and urban envronments. Hgh-densty networks of less expensve remote sensors are capable of operatng ntellgently to ncrease detecton efficency whle controllng costs. If current trends n tech- nologes are a gude, many new nstrumentaton networks wll be composed of ntellgent sensors that can be tasked to nteract wth nearby nodes and self-drected efficent network coverage by standard rules of engagement.

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estABLIsHeD WeAtHeR ReseARcH AnD tRAnsItIonAL neeDs 87 ths concept s beng explored and tested by the cAsA project (McLaughln et al., 2005, 2009). Recommendation: Federal agencies and their partners should deploy a national network of profiling devices for mesoscale weather and chemical weather prediction purposes. Such devices should incorpo- rate capabilities that extend from the subsurface to 2 to 3 km above the surface level. The entire system of observations in support of mesoscale predictions should be coordinated, developed, and evaluated through testbed mechanisms. As a hgh nfrastructure prorty, optcal and rado-frequency profil- ers should be deployed natonally at approxmately 400 stes to con- tnually montor lower tropospherc meteorologcal condtons. to meet natonal needs n support of chemcal weather forecasts, a core set of atmospherc pollutant composton profiles should be obtaned at approxmately 200 urban and rural stes. to meet natonal needs for representatve land–atmosphere latent and sensble heat flux data, a natonal, real-tme network of sol mosture and temperature profile measurements should be made to a nomnal depth of 2 m and deployed natonwde at approxmately 3,000 stes. Federal agences, together wth state, prvate-sector, and nongov- ernmental organzatons, should employ mesoscale testbeds for appled research and development to evaluate and ntegrate natonal mesoscale observng systems, networks thereof, and attendant data assmlaton systems as part of a natonal 3D network of networks. Other Considerations other essental attrbutes and consderatons of a natonal mesoscale observng system nclude the followng: • Augmentaton of observatons n the coastal ocean and marne boundary layer, partcularly where small-scale varablty n sea surface tem- perature gradents and surface waves are clmatologcally common. these quanttes strongly modulate the nterfacal fluxes and therefore tropcal and md-lattude cyclone amplficaton. Whereas smlar satellte observatons are reasonably quanttatve over the open ocean, specal requrements apply to the coastlnes. • the natonal network archtecture needs to be sufficently flexble and open to accommodate auxlary, research-motvated observatons and

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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. the mpact of research-based systems s lkely to be felt at or near the earth’s surface, relevant to both managed and natural terrestral and marne ecosystems, and ssues unque to the heavly bult envronment. A more seamless blendng of formal un- versty educaton wth observatons, operatonal forecastng, and research wll promote the capacty buldng requred to satsfy personnel needs of the future. • extensve metadata wll be requred of every component n an nte- grated, multuse observng system. observatonal data have hgh value only f they are accompaned by comprehensve metadata. Provson of metadata would be needed for partcpaton n a natonal network of networks, and ncentves could be offered to the operators of networks to provde t. the contents of a metadata file would need to be carefully defined and, once assembled, a frequently updated, natonal database of metadata would be accessble to all. If acton s taken to mprove metadata and fill gaps by sup- plyng comprehensve nformaton on undocumented systems, the value and mpact of exstng data wll be mproved far beyond the cost of gatherng the metadata. • stakeholders could commsson an ndependent team of socal and physcal scentsts to conduct an end-user assessment for selected sectors. the assessment could quantfy further the current use and value of mesoscale data n decson makng and also project future trends and the value assoc- ated wth proposed new observatons. Upon mplementaton and utlzaton of mproved observatons, perodc assessments would be conducted to quantfy change n mesoscale data use and the added socetal mpact and value. In addton to the nvolvement of known data provders and users, a less formal survey could capture user comments from blogs and webpage feedback.