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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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these accomplishments, the United States is not the world leader in global numerical weather prediction (NWP). Figure 3.1 indicates that the United States has made steady progress in global weather forecasting performance, but so have other countries.
Within the United States, however, the performance of the NOAA/NWS National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) is superior to the Navy Operational Global Atmospheric Prediction System (NOGAPS) operated by the Fleet Numerical Meteorology and Oceanography Center (FNMOC). In particular, the gap in model performance between NCEP and the European Centre for Medium-Range Weather Forecasts (ECMWF) has not narrowed in the past 15 years. The primary reason is the slow and sometimes ineffective transfer of achievements in the external research community to operational centers in the United States (R2O). Another reason is the lack of investment and progress in assimilating observations in advanced weather prediction models, which is also related to the slow R2O process in data assimilation. In addition, NCEP’s high-performance computing (HPC) capacity, despite recent upgrades, lags behind the capacities of many other major prediction centers around the world.3 The complexities associated with using a hydrostatic global model (GFS) and a variety of regional (nonhydrostatic and hydrostatic) models4 makes it very challenging to maintain and improve these prediction models and associated data assimilation schemes, particularly at the underresourced NCEP. As a consequence, the United States is not fully realizing the potential benefits of its substantial investments in observing systems.
Progress and Remaining Needs
As the horizontal grid spacing of models continues to decrease, especially to less than 10 km, hydrostatic models are no longer appropriate, and it is essential that global nonhydrostatic NWP models (Box 3.1) be coupled with ocean and land models. In fact, Japan’s global Non–hydrostatic Icosahedral Atmospheric Model (NICAM), which runs on the Earth Simulator5 computer, has reached horizontal grid spacing of 3.5 km (Satoh et al., 2008), which results in a spatial resolution 10 times greater (and an areal
3
These and other findings have been discussed in the recently completed external review of NCEP, the “2009 Community Review of National Centers for Environmental Prediction,” that was managed by the University Corporation for Atmospheric Research (UCAR). The executive summary of the NCEP review is included as Appendix B of this report.
4
The NCEP website describes the models operated by NCEP; see http://www.emc.ncep. noaa.gov/.
5
See http://www.jamstec.go.jp/esc/index.en.html.
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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.
resolution 100 times greater) than the 35-km grid spacing of the hydrostatic GFS model at NCEP’s Environmental Modeling Center (EMC).6 ECMWF has also upgraded its operational forecasts to 16-km grid spacing since January 2010.
Now is an optimal time to invest in this area because of many achievements that have been made in the past decade, such as progress in global
6
See http://www.emc.ncep.noaa.gov/gmb/STATS/html/model_changes.html.
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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 successfully 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 spacing 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 conditions 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 contrast, 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, flurries), 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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nonhydrostatic research modeling, progress in data assimilation (including the assimilation of WSR-88D radar reflectivity and radial velocity data over the continental United States), and increased high-performance computing capacity.
A recent achievement is the establishment of various testbeds (e.g., the multiagency, distributed Developmental Testbed Center; and the virtual National Oceanic and Atmospheric Administration [NOAA] Hydrometeorological Testbed [HMT]), which will be helpful for the R2O transition as venues to test new observing and forecast capabilities. The National Aeronautics and Space Administration (NASA)/NOAA/Department of Defense (DOD) Joint Center for Satellite Data Assimilation (JCSDA) has also been established with the goal of accelerating the use of global satellite data in operational forecasting. However, extramural funding to support operationally oriented research at these testbeds is very limited (Mass, 2006).
In addition to the requirement for better weather forecasting models and more efficient and effective data assimilation methods, there is a pressing need for basic research to better understand the inherent predictability of weather phenomena at different temporal and spatial scales, which is also relevant to social scientists whose research and questions often have elements of scale (see Chapter 2). There is also a major emerging weather research question concerning how weather may change in a changing climate. These and similar issues have been raised in various previous studies (e.g., PDT–1 [Emanuel et al., 1995]; PDT–2 [Dabberdt and Schlatter, 1996]; PDT–7 [Emanuel et al., 1997]; NRC, 1998b), but little progress has been achieved. Although the relationship between changes in climate and weather is important both scientifically and practically, it is outside the scope of the present study and this report.
It is now widely recognized that physical processes at the atmosphere– ocean–land interface play a significant role in weather forecasting, such as the impact of atmosphere–wave–ocean coupling on hurricane forecasting (Chen et al., 2007) and land–atmosphere coupling on near-surface air temperature, humidity, turbulent fluxes, convection initiation, and precipitation. The role of biological (e.g., vegetation greenness and leaf area index) and chemical (e.g., trace gases, aerosols) processes in weather and air pollution forecasting has also received increased attention.
Unified Modeling Frameworks and Coupled Modeling
Many global weather forecasting models, such as those at NCEP and ECMWF, are hydrostatic because their grid spacings are generally greater than 10 km or so. In contrast, many regional weather research and forecast
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models (e.g., WRF) are nonhydrostatic. Of particular note are high-resolution nonhydrostatic models (those with grid spacing around 2 km), which would remove the dependence of the model on convective parameterizations, a major barrier for progress in weather forecasting. Global nonhydrostatic NWP models would provide a unified framework for global and regional modeling, and consequently help form a more seamless transition between weather and climate predictions; the importance of a unified framework was recently advocated by the World Climate Research Program in its new strategic plan (WCRP, 2009). The UK Meteorological Office7 has adopted this approach with positive results (Figure 3.1). Such a unified nonhydrostatic modeling system with different configurations (e.g., as a global model with a uniform horizontal resolution, as a global model with two-way interactive finer meshes at specific regions, or as a regional model) has also been developed in the United States (Walko and Avissar, 2008). The nonhydrostatic WRF model has been widely used (there are thousands of registered domestic and international users from public agencies, academia, and the private sector) as a regional model for research and weather forecasting (e.g., NCEP); WRF can also be configured as a global model. However, with a latitude-longitude grid in the global WRF, a polar filter is still required, and a better alternative grid may be the icosahedral grid (see Box 3.1). This development of a unified framework would facilitate and increase the interaction among several communities that have traditionally been segregated—the weather and climate communities, and the regional and global modeling communities. A common model framework would also reduce costs through improved efficiencies and enhanced collaborations in the development of various model physical parameterizations.
High-resolution global nonhydrostatic models have the potential to improve regional modeling because many regional weather prediction and data assimilation problems are essentially global problems; better global models can also reduce the effects of incompatible lateral boundary conditions for the regional models through the use of consistent model physics and two-way nesting. The ability to run both global and regional models in two-way nested mode will also create many new research opportunities (e.g., to study changes in weather in a changing climate and the potential upscaling effects on global circulations).
A number of key capabilities remain to be developed for coupled nonhydrostatic models; they include sufficiently high spatial and temporal resolutions that enable convection and high-impact weather to be explic-
7
See www.metoffice.gov.uk/science/creating/daysahead/nwp/um.html.
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itly resolved (avoiding the limitations of cumulus parameterizations) in global models; assimilation of convective-scale observations using advanced methods that eliminate precipitation spin-up and improve initial conditions in general; improvements in cloud microphysics, physics of the planetary boundary layer (PBL), and interface physics related to the atmospheric coupling to ocean and land processes; and design and development of convective-scale ensemble prediction and post-processing systems for improved and readily interpretable probabilistic forecasts.
Various numerical methods—spectral, finite-difference, and finite-volume—have been used in global hydrostatic weather forecasting models. For global high-resolution nonhydrostatic models, further evaluation of these methods and development of new methods are still needed. New grid cell structures (rather than the traditional latitude-longitude grids) are needed, especially for the treatment at the poles (e.g., Walko and Avissar, 2008). A promising combination might be the finite-volume method with icosahedral grid cells (e.g., Heikes and Randall, 1995). In particular, a new global weather forecasting model with icosahedral horizontal grid, isentropic-sigma hybrid vertical coordinate, and finite-volume horizontal transport (called “FIM”) has been developed at the NOAA Earth Systems Research Laboratory (ESRL).8 The hydrostatic version of FIM is available, but the nonhydrostatic version remains to be developed.
Observations are still inadequate to optimally run and evaluate most high-resolution models and determine forecast skills at various scales. (A detailed discussion of the opportunities and needs for mesoscale observations is provided in the last section in this chapter.) Perhaps even more challenging is the development of suitable and effective verification and evaluation metrics and methods for determining probabilistic forecast skills at different scales.
High-resolution and ensemble forecasts require high performance computing (HPC) capability for model predictions but also for data assimilation, post-processing, and visualization of the unprecedented large volumes of data. HPC facilities are currently available at some Department of Energy (DOE) and NASA centers, as well as at NSF-sponsored centers, which are usually used for research and climate simulations. It would be beneficial to have an HPC center that is dedicated to the support of weather forecasting and research in the academic and related research community to facilitate R2O activities. HPC facilities are also suboptimal within NOAA for operational weather forecasting. A substantial increase in computing capacity
8
See http://fim.noaa.gov.
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dedicated to the operational and research communities for the high-resolution weather modeling enterprise is required (see Appendix B). The recent NOAA partnership with the DOE Oak Ridge National Laboratory on HPC support (with a focus on climate research and prediction) will help alleviate the HPC demand at NCEP. Also crucial is development of improved software to increase the computational efficiency and scalability of the forecast models as well as post-processing and visualization, particularly at petascale (1015) HPC (e.g., NRC, 2008c).
Data Assimilation and Observations
Data assimilation as part of the forecast system is also important for acquiring and maintaining observing systems that provide the optimal cost-benefit ratio to different user groups and their applications. Data denial experiments can selectively withhold data from one (or more) system(s) and assess the degradation in forecast skill. Data assimilation can be used to determine the optimal mix of current and future in situ and remotely sensed measurements, and also for adaptive or targeted observations (e.g., Langland, 2005). It is also beneficial to understand the impacts of observing systems on model performance and the resulting forecast accuracy (e.g., Gelaro and Zhu, 2009; Rabier et al., 2008). With aging satellites in space and insufficient satellites in the NASA and NOAA pipelines to replace or enhance them, observing system simulation experiments can also help support detailed cost-benefit analyses.
Besides the satellite and radiosonde data that are widely used in global operational NWP, the assimilation of data from radar and other sources is also crucial, particularly for regional forecasting. Preliminary work done at the University of Oklahoma (Xue et al., 2009) indicates that a high-resolution regional model initialized using global model output has relatively large initial errors in precipitation forecasting but these errors do not further increase with time in the first few hours. On the other hand, their results also indicate that regional precipitation forecasting with radar data assimilation has smaller initial errors but they increase rapidly with time in the first few hours, as expected from our understanding of atmospheric predictability.
For data assimilation in high-resolution, cloud-resolving, and coupled air–sea–land models, it is particularly important to address the inconsistency in model physics and observations. For instance, the cloud droplet size distribution assumed in models may not be the same as that assumed in satellite-retrieved cloud properties. Although significant progress has been made in data assimilation using the individual model component of the atmosphere,
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ocean, or land, progress is lacking with the fully coupled system. There is also a lack of coherent observations across the atmosphere–ocean–land interface for data assimilation in the fully coupled models.
It is important to critically compare different advanced data assimilation methods, (e.g., the ensemble Kalman filter [EnKF] and 4-dimensional variational [4DVar] analysis) with those methods currently used by some operational models such as 3-dimensional variational (3DVar) approaches. For instance, the Navy’s FNMOC recently initiated operational use of the Naval Research Laboratory’s 4DVar data assimilation system to replace the 3DVar in NOGAPS. Preliminary impact tests (Xu and Baker, 2009) indicate that equivalent 5-day forecast skill with the 4DVar system is extended about 9 hours in the Southern Hemisphere and 4 hours in the Northern Hemisphere, and tropical cyclone 5-day track forecast errors are reduced. Similarly, the experiments at the NOAA ESRL (MacDonald, 2009) indicate that the EnKF improves global weather forecasts compared with the 3DVar system. A community consensus is emerging that the future of data assimilation may belong to a hybrid EnKF-4DVar system (e.g., Zhang et al., 2009); the pace in testing and implementing such a hybrid system needs to be accelerated at NCEP.
Model advances—such as improved grid resolution, model physics, and data assimilation—also improve the performance of short-range, 0- to 12-hour forecasts. These forecasts are of high societal relevance for many applications, such as forecasting severe weather (warning the public and protecting lives and property), wind speed/direction changes (improving the use of wind-generated power), solar radiation (for solar power generation), visibility (for surface and aviation transport), and air pollution (for public health). Such forecasts also demand and stimulate the development of new observing technologies and measurements, such as the high-performance, low-cost, polarimetric X-band radar networks being developed within the Collaborative Adaptive Sensing of the Atmosphere (CASA) program (McLaughlin et al., 2007, 2009).
Observational data with high temporal and spatial resolution are crucial to the understanding of atmospheric processes, providing data for assimilation in models, and evaluating and improving those models. This requires the synergistic combination of data from diverse sources. Rawinsonde, radar, satellite, and aircraft data as well as data from other sources all play complementary roles in weather research and forecasting.
Rawinsonde coverage needs to be maintained and enhanced because of its value for weather forecasting and evaluation of satellite data. Geostationary satellites provide excellent temporal coverage, but new technologies are
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still required to add passive microwave sensors that can penetrate through clouds. Polar orbiting satellites provide global coverage with better spatial resolution, but active microwave, infrared, radio frequency, and optical sensors are still needed to provide data on the three-dimensional structure of the atmosphere. Soundings of refractive index (as a function of atmospheric temperature and the partial pressures of dry air and water vapor) from global positioning system (GPS) satellites have also proved very useful for weather forecasting. NEXRAD (WSR 88-D) radars provide excellent precipitation detection above the PBL over relatively flat surfaces, but their spatial coverage—especially over the western United States—is far from complete, and some radars are not optimally sited for weather research and forecasting. The increase in the number of radar sites and technological upgrades (e.g., the planned dual-polarization capability) are very much needed. The massive numbers of surface networks need to be effectively used in data assimilation. Similarly, methods to make good use of the large numbers of automated meteorological reports from commercial aircraft need to be developed (Moninger et al., 2003). More detailed discussion on mesoscale observations are provided in the last section of this chapter.
Finally, a national capacity needs to be developed for optimizing the transitioning of environmental observations from research to operations. Such a capacity is still lacking at present (NRC, 2009b). A rational five-step procedure for configuring an optimal observing system for weather prediction was proposed a decade ago by a prospectus development team, PDT–7 (Emanuel et al., 1997) and remains relevant today. Briefly summarized, the procedure involves identifying specific forecast problems; using contemporary modeling techniques; estimating the incremental forecast improvements; estimating the overall cost (to the nation, rather than to specific federal agencies); and using standard cost-benefit analyses to determine the optimal 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 modeling systems should have the capability for different configurations: as a global model with a uniform horizontal resolution; as a global model with two-way interactive finer grids over specific regions; and as a regional model with one-way coupling to various global models. Also required are improved atmospheric, oceanic, and
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land observations, as well as significantly increased computational resources to support the development and implementation of advanced data assimilation systems such as 4DVar, EnKF, and hybrid 4DVar–EnKF approaches.
Predictability
Intrinsic predictability of the atmosphere–ocean–land system is a fundamental research issue. Even though predictability has been studied for the past half century and was a major theme in the Stormscale Operational and Research Meteorology (STORM) documents in the 1980s (e.g., NCAR, 1984), not much is known today about the inherent limits to predictability of various weather phenomena at different spatial and temporal scales. Because of the error growth across all scales, from cumulus convection to mesoscale weather and large-scale circulations, a high-resolution (preferably cloud-resolvable) nonhydrostatic global model is crucial to address such error growth and better understand the predictability of weather systems. Although predictability is obviously important to operational forecasting, increased emphasis on basic science (such as the limits of predictability) would be beneficial to the greater weather community.
Another fundamental question of predictability is error growth across various scales. This issue is particularly important as higher-resolution non-hydrostatic global models are developed. For instance, how up-scaling error growth from convective scales affects the larger scale circulation is poorly understood in both regional and global models. Some recent modeling experiments indicate that increasing model resolution may not improve forecasting skill for the first few days but may improve forecasts for days 3 through 5 (MacDonald, 2009).
Actual predictive skill may likely be dependent on the specific phenomenon (e.g., mesoscale convective systems [MCSs] versus tornadoes). It is difficult to assess predictive skill, because the lack of skill can result from problems arising from data and data assimilation deficiencies, errors in numerical representation, intrinsic predictability limitations, and forecast verification methodology. Retrospective forecasts have been found to be helpful in better understanding forecast errors and improving global forecast skills (Hamill et al., 2006). To address both intrinsic predictability and predictive skill, global nonhydrostatic modeling can be helpful. If such models are used operationally and if they become user-friendly and available to the research community, researchers will be able to assist in diagnosing the sources of errors by rerunning modeling cases with large (and small)
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and Environmental Research Systems (WATERS) Network initiative (NRC, 2009a) proposes a series of observatories to address the challenges of ecosystem sustainability and water availability under changes caused by human activities and climatic trends.19
Unique opportunities to use remotely sensed observations from surface and space require exploration (e.g., Yilmaz et al., 2005). These include currently available data (e.g., see the following section on observations) and the new stream of data that will be delivered by NASA’s Global Precipitation Measuring mission as well as the satellite soil moisture measurements of the Soil Moisture Active-Passive.20 The use of satellite observations for improved hydrologic prediction offers ample research challenges and opportunities, such as retrieval algorithm development over land, multichannel integration, bias adjustment, and increasing accuracy and resolution via product blending. Remotely sensed observations are the only observations for many parts of the world and their optimal use in hydrologic forecasting needs to be fully explored. In the United States, the ongoing upgrade of the NWS NEXRAD radars to dual-polarimetric capability will significantly improve the ability to resolve hydrometeorologic variables important for the development of coupled atmospheric–hydrologic models. Also, as clearly articulated in NRC (2009b) and discussed in the following section, a ground-based national network of networks at the mesoscale, inclusive of radio and optically sensed PBL profiles of water vapor and winds is expected to dramatically improve our predictive ability of the coupled atmospheric–hydrologic 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 supported to accelerate fundamental understanding of water cycle dynamics; 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 investment in improved weather and climate forecasts into improved hydrologic forecasts at local and regional scales, and meet the pressing societal, economic, and environ-
19
The overarching science question of the WATERS Network is: “How can we protect ecosystems and better manage and predict water availability and quality for future generations, given changes to the water cycle caused by human activities and climate trends?” (WATERS, 2009).
20
See: http://nasascience.nasa.gov/earth-science/decadal-surveys/Volz1_SMAP_11-20-07.pdf.
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mental demands of water availability (floods, droughts, and adequate water supply for people, agriculture, and ecosystems), an accelerated hydrologic research and R2O strategy is needed. Fundamental research is required on the physical representation of water cycle dynamics from the atmosphere to the subsurface, probabilistic prediction and uncertainty estimation, assimilation of multisensor observations, and model verification over a range of scales. Integral to this research are integrated observatories (from the atmosphere to the land and to the subsurface) across multiple scales and hydrologic regimes.
Continuous support of individual models geared toward improving specific components of the hydrologic cycle is a necessary element of progress. However, the committee recommends that a community organization around benchmark hydrologic and coupled atmospheric–hydrologic modeling systems is necessary at this stage to further advance hydrologic predictions, in both research and operational modes. Such benchmark models will provide for the synthesis of ideas and data, avoid duplication, identify major research gaps, provide metrics of success and track progress, and provide a platform on which communities can share knowledge (e.g., there is a need for ecologists to have access to hydrologic models, and a community-led framework would be useful in the same way that WRF has been useful to decision makers across widely diverse sectors). Also, such a community-based modeling framework will accelerate the development of a focused R2O and O2R strategy based on a sustained and effective academia–industry–government partnership.
MESOSCALE OBSERVATIONAL NEEDS
Improved observing capabilities at the mesoscale are an explicit aspect of every weather priority identified in this study, including socioeconomic priorities such as reduction in vulnerability for dense coastal populations, and improvements in forecasts at the scale of flash floods and routinely disruptive local weather. The 2009 BASC Summer Study workshop participants identified the underlying need for enhanced mesoscale observing networks throughout the oral presentations and in the working group discussions. This high-priority 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 its authors were participants in the 2009 BASC Summer Study workshop. Accordingly, this section draws heavily on the findings and recommendations contained in that report and sup-
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ports each of its recommendations concerning observing-system technical requirements.
Observations at the mesoscale are an important part of the national and global observing systems. The global observing system captures large-scale circulation features and their related thermodynamic context. Components of the global observing system include various satellites and constellations thereof; radiosondes and regional electromagnetic profiling devices; and reference surface stations at key locations that maintain and extend the surface climate record. For example, geostationary satellites provide excellent temporal coverage, but new technologies are needed to penetrate clouds for thermodynamic information. Observations based on GPS constellation occultation techniques contribute important temperature and humidity information, principally at the synoptic scale. Observations at the mesoscale are increasingly important to establishing initial conditions for global models as resolution improves.
Mesoscale components include observations that either resolve mesoscale atmospheric structure or uniquely enable NWP at the mesoscale. The emphasis on mesoscale observations is motivated by the scale and phenomenology associated with disruptive weather; the necessity to understand it, detect it, and warn of the potential consequences; and improved capacity to specifically predict or otherwise anticipate it at very short to short ranges (0 to 48 hours).
Challenge: Why Are Enhanced Mesoscale Observations Needed?
Perhaps the simplest answer to this question is that high-impact weather happens at the mesoscale, and because the lowest 2 to 3 km of the atmosphere are, at once, underobserved and most important for processes such as convection, chemical transport, and the determination of winter precipitation type.
A more complete justification for mesoscale observations resides in the requirements to serve a wide variety of stakeholders inclusive of basic and applied researchers, intermediate users associated with weather-climate information providers, and a wide variety of end users at all levels of government and numerous commercial sectors. Some examples of these requirements include
Basic research in the geosciences and biogeosciences, including studies of mesoscale dynamics, gravity waves, climate science, atmospheric chemistry, micrometeorology, hydrometeorology, cloud physics, atmo-
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spheric electricity, biogeochemistry, ecosystems, biogenic emissions, and urban-scale processes;
NWS-related R2O activities to enable very short- and short-range predictions that employ advanced nowcasting techniques in the 0- to 3-hour range; to improve analyses of initial and boundary conditions and short-range predictions for mesoscale model forecasts in the 12- to 48-hour range; to enable the merging of probabilistic guidance associated with nowcasting and dynamical predictions in the 3- to 12-hour range; to provide a basis for object-oriented verification of probabilistic forecasts resulting from ensemble techniques; and to facilitate technique development for advanced applications of mesoscale observations to locally disruptive weather such as fog, surface icing, thunderstorm initiation and motion, assimilation of precipitation measurements, conditions near hurricane landfall, other hazardous lake and coastal ocean conditions, hazardous urban conditions, fire weather predictions, and hydrologic predictions and warnings such as seasonal flooding from main stem rivers and flash floods;
Directly serving the missions of numerous federal, state, and local agencies including NOAA, the Department of Transportation, DOD, the Environmental Protection Agency, DOE, USGS, the Department of Agriculture, NSF, the Department of Homeland Security, and NASA at the federal level; and several agencies in all 50 states, including transportation, emergency management, water resources, and air quality applications;
Directly serving and improving productivity in commercial sectors such as renewable (discussed in detail in Chapter 4) and conventional energy production industries; agricultural cooperatives and suppliers; the commercial air, sea, and land transportation industries; weather and climate information corporations; broadcast media; commodities exchange; insurance/reinsurance industries; among many others.
Progress in the Past Decade
As reported in NRC (2009b), mesoscale surface observations have proliferated enormously in the past decade; so much so that the mesoscale surface observations enterprise is ubiquitous across approximately 20 federal agencies, all 50 states, countless local water and air quality districts and authorities, numerous Fortune 500 corporations, countless small- and mediumsized commercial applications, the private weather information industry, and others. However, progress in application of these data is impeded (NRC, 2009b) by a lack of cohesion, coordination, and knowledge of standards,
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thus limiting most surface networks to a single use status when much of the same data could, in principle, serve multiple national needs.
Despite the proliferation of surface stations, there continues to be a paucity of vertical profile data, which are badly needed to support monitoring of the PBL from 2 m below the land surface to 2 km above; and to enable improved weather, chemical weather, and hydrologic predictions.
Research and Development Topics Dependent on Mesoscale Observations
Mesoscale Data Assimilation
There is a pressing need for research and development leading to improved mesoscale data assimilation techniques in operational forecast systems. Improved analyses require better knowledge of error covariances in observations. The basis for this knowledge is weakened by the fact that mesoscale data are often sparse or patchy, and relatively poorly documented compared to those from standard synoptic observations. The structure and variability of the lower troposphere is not well known owing to the fact that vertical profiles of water vapor, temperature, and winds are not systematically observed at the mesoscale (Schlatter et al., 2005). The sensitivity to these observation gaps is not well understood but is likely to be substantial in urban (Dabberdt et al., 2000) and coastal (Droegemeier et al., 2000) regions, where population density is high, and in mountainous regions, which are a proximate cause for major forecast errors (“busts”) downstream (Smith et al., 1997). The relative absence of high-resolution PBL profiles is a vexing problem that greatly impedes progress in skillful predictions at the mesoscale over both land and coastal waters. Whereas mesoscale weather events can be produced by forecast models from purely synoptic-scale initial conditions, mesoscale predictability is also dependent, to some considerable degree, on knowledge of the mesoscale initial condition. This is especially true with respect to specific predictions of deep moist convection and attendant heavy rainfall and severe weather (Fritsch and Carbone, 2004).
Verification
Research and development are needed that lead to improved forecast verification and from which errors in the forecast system can be quantified, understood, and rectified. Especially with respect to verification of precipitation, statistical scores such as equitable threat can be misleading and
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are relatively uninformative at the mesoscale. Unlike threat scores, object-oriented approaches (e.g., Davis et al., 2006a, b) can allow for both intermittency and relatively small spatial and temporal uncertainties. This permits the quantification of errors and enables a useful characterization of skill in predictions within regions containing, for example, rain bands, mesoscale convective systems, and isolated storms.
For example, current operational verification techniques are insufficient, for hydrologic predictions. There exists considerable sensitivity to smallscale variability in precipitation spatial distribution; the probability density function of precipitation rate; subsequent melt rate in the case of ice-phase precipitation; and surface losses such as evaporation of liquid water and sublimation of snow and ice. Several of these factors argue for verification methods that can better inform the hydrologic user of precipitation estimates and forecast data than an equitable threat score. Object-oriented methods can include specific location, size, shape, rate, and translation information in addition to cumulative amount. Both improved observations, such as those from polarimetric radar, and catchment- and convection-resolving models are necessary to achieve this level of sophistication, as well as this level of verification and near-real-time input to distributed hydrologic models.
Surface and Boundary-Layer Fluxes
Research is needed as well that leads to improved knowledge and representation of meteorological and chemical fluxes, emissions, and deposition in high-resolution weather and climate system models. This includes natural and polluted terrestrial boundary layers; marine boundary layers; land– atmosphere exchange dependencies on canopy properties, soil moisture and temperature; urban surface energy exchange and emissions; upper ocean heat content and surface wave properties; biogenic emissions of volatile organic compounds; gas phase-to-aerosol conversion, total aerosol burden, and its vertical distribution and transport. Several of these fluxes are either known or suspected to modulate precipitation and the harmful effects of pollution.
Observing System Testbeds
It is important that mesoscale observations are a focus of testbeds, which are intended to develop and introduce new ideas and new procedures in environmental observation. For example, advanced concepts in mobile, targeted, adaptive, and collaborative observing networks require extensive
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exploration. This includes the dynamics among various surface-based observing networks as well as the exploration of optimal and cost-effective divisions of responsibility between observations from space and those from airborne and surface platforms.
Forecast System Testbeds
Another focus of testbeds can be to examine the role of mesoscale observations for new paradigms in the end-to-end forecast process. This is particularly important and urgent with respect to merging methods in nowcasting with those of dynamical prediction in the 0- to 6-hour range. This strategy may be central to improved performance in severe weather, flash floods, other hydrologic forecasts, and metropolitan area applications in routinely disruptive weather.
Mesoscale Observing Needs
Atmospheric and Subsurface Profiles
These mesoscale observing needs and recommendations consolidate and follow the general sense of recommendations of NRC (2009b), where more detailed discussion and analysis can be found. The highest priority observations needed to address current inadequacies include those items for which there are essentially no systematic national capabilities to resolve or enable mesoscale prediction; these include
height of the PBL,
soil moisture and temperature profiles,
high-resolution vertical profiles of humidity, and
profiles of air quality and related chemical composition above the surface layer.
Improvements are also needed in measurements of direct and diffuse solar radiation, wind profiles, temperature profiles, surface turbulence, subsurface temperature profiles, and near-surface icing.
Humidity, wind, and diurnal boundary layer structure profiles are the highest priority for a national mesoscale network, the sites for which need to have a characteristic spacing of approximately 150 km but could vary between 50 and 200 km based on regional considerations. Such observations, although not fully mesoscale resolving, are essential to enable improved
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performance by high-resolution NWP models and chemical weather prediction at the mesoscale. Through advanced data assimilation techniques, it is estimated (NRC, 2009b) that data from approximately 400 sites, when used in combination with advanced geostationary satellite infrared and microwave soundings, GPS constellation “wet delay” and radio occultation measurements, and commercial aircraft soundings, will effectively fill many of the critical gaps in the national mesoscale observing system.
Among observations from space, the geostationary platform component is especially important for water vapor and clouds because it preserves the integrity of time-domain sampling with respect to mesoscale variability in the lower troposphere. Owing to the high orbit, visible and infrared instruments are preferred in maintaining horizontal resolution at the expense of obscuration by clouds. Notably, NRC (2009b) recommends both infrared hyperspectral instruments as well as synthetic thinned aperture array instruments at cloud-penetrating microwave frequencies on geostationary platforms. A reasonable performance expectation is for satellite observations to assume a primary role at altitudes above the continental PBL.
The core set of air quality and atmospheric composition profiles above the atmospheric surface layer would include measurements of carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3), and particulate matter less than 2.5 microns in size (PM2.5); these chemical composition profiles are needed at about 200 sites, which would yield a characteristic spacing of approximately 200 km. These observations would constitute a national pollutant constituent backbone and would be especially effective in enabling air quality (chemical weather) prediction when collocated with surface meteorological observations and related vertical profiles. The selected core chemical species have various impacts (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 utility of parameters observed from space. Additional important parameters (e.g., NO2) could be added as soon as appropriate and when affordable technology is developed for the applications envisioned. The proposed network would improve chemical weather prediction nationally and also support urban air pollution monitoring, for which it is not a substitute.
Soil moisture and temperature measurements are needed to a depth of 2 m at a characteristic spacing of about 50 km, which corresponds to about 3,000 multiple-sample or area-integrated sites. These soil measurements are required, together with surface atmospheric measurements, to quantify surface fluxes of latent and sensible heat. Although this spacing is insufficient to capture the full spectrum of short-term spatial variability of surface soil
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moisture and temperature, it is small enough to represent seasonal variations and regional gradients, thereby supporting numerous important applications such as land data assimilation systems in support of NWP, water resource management, flood control and hydrologic forecasting, and management of forestry, rangeland, cropland, and ecosystems.
Network Architecture and Testbeds
To serve multiple national needs, the United States needs a system that is a network of networks in an architectural sense (NRC, 2009b). The term “architecture” includes the fundamental physical elements as well as the organizational and interfacial structure of the mesoscale network. It also describes the internal interfaces among the system’s components, and the interface between the system and its environment, especially the users. This architecture would facilitate a thriving environment for data providers and users by promoting metadata, standards, and interoperability, and enabling access to mesoscale data, analysis tools, and models. The effort would also include a process that continually identifies critical observational gaps, new measurement systems and opportunities, and the evolving requirements of end users.
Applied research and development would include but not be limited to transitional activities, including the operation of prototype networks and evaluation of their forecast impact (i.e., testbeds); development of tools to facilitate data access for real-time assimilation; development of additional tools to serve the general public and educate the citizenry; and exploration of advanced and innovative technologies to serve multiple national needs better, cheaper, and sooner than otherwise might be possible. Testbeds may be operated by national laboratories, universities, or joint institutes as appropriate to the application. Such activities are inherently multidisciplinary and must be tightly coordinated if R2O objectives are to be achieved. Testbeds may have a sharply focused, limited term of activity that fully integrates users in the transition to operations.
Collaborative and adaptive sensing and related technologies can efficiently enhance the detection and monitoring of adverse weather for hazard mitigation and other applications, particularly for convective scales and in complex terrain, coastal, and urban environments. High-density networks of less expensive remote sensors are capable of operating intelligently to increase detection efficiency while controlling costs. If current trends in technologies are a guide, many new instrumentation networks will be composed of intelligent sensors that can be tasked to interact with nearby nodes and self-directed efficient network coverage by standard rules of engagement.
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This concept is being explored and tested by the CASA project (McLaughlin 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 incorporate 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 high infrastructure priority, optical and radio-frequency profilers should be deployed nationally at approximately 400 sites to continually monitor lower tropospheric meteorological conditions. To meet national needs in support of chemical weather forecasts, a core set of atmospheric pollutant composition profiles should be obtained at approximately 200 urban and rural sites. To meet national needs for representative land–atmosphere latent and sensible heat flux data, a national, real-time network of soil moisture and temperature profile measurements should be made to a nominal depth of 2 m and deployed nationwide at approximately 3,000 sites.
Federal agencies, together with state, private-sector, and nongovernmental organizations, should employ mesoscale testbeds for applied research and development to evaluate and integrate national mesoscale observing systems, networks thereof, and attendant data assimilation systems as part of a national 3D network of networks.
Other Considerations
Other essential attributes and considerations of a national mesoscale observing system include the following:
Augmentation of observations in the coastal ocean and marine boundary layer, particularly where small-scale variability in sea surface temperature gradients and surface waves are climatologically common. These quantities strongly modulate the interfacial fluxes and therefore tropical and mid-latitude cyclone amplification. Whereas similar satellite observations are reasonably quantitative over the open ocean, special requirements apply to the coastlines.
The national network architecture needs to be sufficiently flexible and open to accommodate auxiliary, research-motivated observations and
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educational needs, often for limited periods in limited regions. If history is a proper judge, many of the research-motivated sensors and observations will evolve to operational status, serving existing societal needs better and serving future additional societal needs well. The impact of research-based systems is likely to be felt at or near the Earth’s surface, relevant to both managed and natural terrestrial and marine ecosystems, and issues unique to the heavily built environment. A more seamless blending of formal university education with observations, operational forecasting, and research will promote the capacity building required to satisfy personnel needs of the future.
Extensive metadata will be required of every component in an integrated, multiuse observing system. Observational data have high value only if they are accompanied by comprehensive metadata. Provision of metadata would be needed for participation in a national network of networks, and incentives could be offered to the operators of networks to provide it. The contents of a metadata file would need to be carefully defined and, once assembled, a frequently updated, national database of metadata would be accessible to all. If action is taken to improve metadata and fill gaps by supplying comprehensive information on undocumented systems, the value and impact of existing data will be improved far beyond the cost of gathering the metadata.
Stakeholders could commission an independent team of social and physical scientists to conduct an end-user assessment for selected sectors. The assessment could quantify further the current use and value of mesoscale data in decision making and also project future trends and the value associated with proposed new observations. Upon implementation and utilization of improved observations, periodic assessments would be conducted to quantify change in mesoscale data use and the added societal impact and value. In addition to the involvement of known data providers and users, a less formal survey could capture user comments from blogs and webpage feedback.
