Abstract

As operational forecast and data assimilation (DA) systems evolve, observing system simulation experiment (OSSE) systems must evolve in parallel. Expected development of operational systems—especially the use of data that are currently not used or are just beginning to be used, such as all-sky and surface-affected microwave radiances—will greatly challenge our ability to construct realistic OSSE systems. An additional set of challenges will arise when future DA systems strongly couple the different Earth system components. In response, future OSSE systems will require coupled models to simulate nature and coupled observation simulators. The requirements for future evolving OSSE systems and potential solutions to satisfy these requirements are discussed. It is anticipated that in the future the OSSE technique will be applied to diverse and coupled domains with the use of increasingly advanced and sophisticated simulations of nature and observations.

Abstract

The ocean surface wind mediates exchanges between the ocean and the atmosphere. These air–sea exchange processes are critical for understanding and predicting atmosphere, ocean, and wave phenomena on many time and space scales. A cross-calibrated multiplatform (CCMP) long-term data record of satellite ocean surface winds is available from 1987 to 2008 with planned extensions through 2012. A variational analysis method (VAM) is used to combine surface wind data derived from conventional and in situ sources and multiple satellites into a consistent nearglobal analysis at 25-km resolution, every 6 h. The input data are cross-calibrated wind speeds derived from the Special Sensor Microwave Imager (SSM/I; F08–F15), the Tropical Rainfall Measuring Mission Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), and wind vectors from SeaWinds on the NASA Quick Scatterometer (QuikSCAT) and on the second Japanese Advanced Earth Observing Satellite (ADEOS- 2; i.e., the Midori-2 satellite). These are combined with ECMWF reanalyses and operational analyses by the VAM. VAM analyses and derived data are currently available for interested investigators through the Jet Propulsion Laboratory (JPL) Physical Oceanography Distributed Active Archive Center (PO.DAAC). This paper describes the methodology used to assimilate the input data along with the validation and evaluation of the derived CCMP products.

A supplement to this article is available online:

DOI: 10.1175/2010BAMS2946.2

A multiscale modeling framework (MMF), which replaces the conventional cloud parameterizations with a cloud-resolving model (CRM) in each grid column of a GCM, constitutes a new and promising approach for climate modeling. The MMF can provide for global coverage and two-way interactions between the CRMs and their parent GCM. The CRM allows for explicit simulation of cloud processes and their interactions with radiation and surface processes, and the GCM allows for global coverage.

A new MMF has been developed that is based on the NASA Goddard Space Flight Center (GSFC) finite-volume GCM (fvGCM) and the Goddard Cumulus Ensemble (GCE) model. This Goddard MMF produces many features that are similar to another MMF that was developed at Colorado State University (CSU), such as an improved surface precipitation pattern, better cloudiness, improved diurnal variability over both oceans and continents, and a stronger propagating Madden-Julian oscillation (MJO) compared to their parent GCMs using traditional cloud parameterizations. Both MMFs also produce a large and positive precipitation bias in the Indian Ocean and western Pacific during the Northern Hemisphere summer. However, there are also notable differences between the two MMFs. For example, the CSU MMF simulates less rainfall over land than its parent GCM. This is why the CSU MMF simulated less overall global rainfall than its parent GCM. The Goddard MMF simulates more global rainfall than its parent GCM because of the high contribution from the oceanic component. A number of critical issues (i.e., the CRM's physical processes and its configuration) involving the Goddard MMF are discussed in this paper.

Abstract

Over a two-year period beginning in 2015, a panel of subject matter experts, the Space Platform Requirements Working Group (SPRWG), carried out an analysis and prioritization of different space-based observations supporting the National Oceanic and Atmospheric Administration (NOAA)’s operational services in the areas of weather, oceans, and space weather. NOAA leadership used the SPRWG analysis of space-based observational priorities in different mission areas, among other inputs, to inform the Multi-Attribute Utility Theory (MAUT)-based value model and the NOAA Satellite Observing Systems Architecture (NSOSA) study. The goal of the NSOSA study is to develop candidate satellite architectures for the era beginning in approximately 2030. The SPRWG analysis included a prioritized list of observational objectives together with the quantitative attributes of each objective at three levels of performance: a threshold level of minimal utility, an intermediate level that the community expects by 2030, and a maximum effective level, a level for which further improvements would not be cost effective. This process is believed to be unprecedented in the analysis of long-range plans for providing observations from space. This paper describes the process for developing the prioritized objectives and their attributes and how they were combined in the Environmental Data Record (EDR) Value Model (EVM). The EVM helped inform NOAA’s assessment of many potential architectures for its future observing system within the NSOSA study. However, neither the SPRWG nor its report represents official NOAA policy positions or decisions, and the responsibility for selecting and implementing the final architecture rests solely with NOAA senior leadership.

Abstract

The Cyclone Global Navigation Satellite System (CYGNSS) is a new NASA earth science mission scheduled to be launched in 2016 that focuses on tropical cyclones (TCs) and tropical convection. The mission’s two primary objectives are the measurement of ocean surface wind speed with sufficient temporal resolution to resolve short-time-scale processes such as the rapid intensification phase of TC development and the ability of the surface measurements to penetrate through the extremely high precipitation rates typically encountered in the TC inner core. The mission’s goal is to support significant improvements in our ability to forecast TC track, intensity, and storm surge through better observations and, ultimately, better understanding of inner-core processes. CYGNSS meets its temporal sampling objective by deploying a constellation of eight satellites. Its ability to see through heavy precipitation is enabled by its operation as a bistatic radar using low-frequency GPS signals. The mission will deploy an eight-spacecraft constellation in a low-inclination (35°) circular orbit to maximize coverage and sampling in the tropics. Each CYGNSS spacecraft carries a four-channel radar receiver that measures GPS navigation signals scattered by the ocean surface. The mission will measure inner-core surface winds with high temporal resolution and spatial coverage, under all precipitating conditions, and over the full dynamic range of TC wind speeds.

Abstract

The prediction of tropical cyclone rapid intensification is one of the most pressing unsolved problems in hurricane forecasting. The signatures of gravity waves launched by strong convective updrafts are often clearly seen in airglow and carbon dioxide thermal emission spectra under favorable atmospheric conditions. By continuously monitoring the Atlantic hurricane belt from the main development region to the vulnerable sections of the continental United States at high cadence, it will be possible to investigate the utility of storm-induced gravity wave observations for the diagnosis of impending storm intensification. Such a capability would also enable significant improvements in our ability to characterize the 3D transient behavior of upper-atmospheric gravity waves and point the way to future observing strategies that could mitigate the risk to human life caused by severe storms. This paper describes a new mission concept involving a midinfrared imager hosted aboard a geostationary satellite positioned at approximately 80°W longitude. The sensor’s 3-km pixel size ensures that the gravity wave horizontal structure is adequately resolved, while a 30-s refresh rate enables improved definition of the dynamic intensification process. In this way the transient development of gravity wave perturbations caused by both convective and cyclonic storms may be discerned in near–real time.

Test beds have emerged as a critical mechanism linking weather research with forecasting operations. The U.S. Weather Research Program (USWRP) was formed in the 1990s to help identify key gaps in research related to major weather prediction problems and the role of observations and numerical models. This planning effort ultimately revealed the need for greater capacity and new approaches to improve the connectivity between the research and forecasting enterprise.

Out of this developed the seeds for what is now termed “test beds.” While many individual projects, and even more broadly the NOAA/National Weather Service (NWS) Modernization, were successful in advancing weather prediction services, it was recognized that specific forecast problems warranted a more focused and elevated level of effort. The USWRP helped develop these concepts with science teams and provided seed funding for several of the test beds described.

Based on the varying NOAA mission requirements for forecasting, differences in the organizational structure and methods used to provide those services, and differences in the state of the science related to those forecast challenges, test beds have taken on differing characteristics, strategies, and priorities. Current test bed efforts described have all emerged between 2000 and 2011 and focus on hurricanes (Joint Hurricane Testbed), precipitation (Hydrometeorology Testbed), satellite data assimilation (Joint Center for Satellite Data Assimilation), severe weather (Hazardous Weather Testbed), satellite data support for severe weather prediction (Short-Term Prediction Research and Transition Center), mesoscale modeling (Developmental Testbed Center), climate forecast products (Climate Testbed), testing and evaluation of satellite capabilities [Geostationary Operational Environmental Satellite-R Series (GOES-R) Proving Ground], aviation applications (Aviation Weather Testbed), and observing system experiments (OSSE Testbed).

The three-dimensional global wind field is the most important remaining measurement needed to accurately assess the dynamics of the atmosphere. Wind information in the tropics, high latitudes, and stratosphere is particularly deficient. Furthermore, only a small fraction of the atmosphere is sampled in terms of wind profiles. This limits our ability to optimally specify initial conditions for numerical weather prediction (NWP) models and our understanding of several key climate change issues.

Because of its extensive wind measurement heritage (since 1968) and especially the rapid recent technology advances, Doppler lidar has reached a level of maturity required for a space-based mission. The European Space Agency (ESA)'s Atmospheric Dynamics Mission Aeolus (ADM-Aeolus) Doppler wind lidar (DWL), now scheduled for launch in 2015, will be a major milestone.

This paper reviews the expected impact of DWL measurements on NWP and climate research, measurement concepts, and the recent advances in technology that will set the stage for space-based deployment. Forecast impact experiments with actual airborne DWL measurements collected over the North Atlantic in 2003 and assimilated into the European Centre for Medium-Range Weather Forecasts (ECMWF) operational model are a clear indication of the value of lidar-measured wind profiles. Airborne DWL measurements collected over the western Pacific in 2008 and assimilated into both the ECMWF and U.S. Navy operational models support the earlier findings.

These forecast impact experiments confirm observing system simulation experiments (OSSEs) conducted over the past 25–30 years. The addition of simulated DWL wind observations in recent OSSEs performed at the Joint Center for Satellite Data Assimilation (JCSDA) leads to a statistically significant increase in forecast skill.

The Atmospheric Infrared Sounder (AIRS) and its two companion microwave sounders, AMSU and HSB were launched into polar orbit onboard the NASA Aqua Satellite in May 2002. NASA required the sounding system to provide high-quality research data for climate studies and to meet NOAA's requirements for improving operational weather forecasting. The NOAA requirement translated into global retrieval of temperature and humidity profiles with accuracies approaching those of radiosondes. AIRS also provides new measurements of several greenhouse gases, such as CO₂, CO, CH₄, O₃, SO₂, and aerosols.

The assimilation of AIRS data into operational weather forecasting has already demonstrated significant improvements in global forecast skill. At NOAA/NCEP, the improvement in the forecast skill achieved at 6 days is equivalent to gaining an extension of forecast capability of six hours. This improvement is quite significant when compared to other forecast improvements over the last decade. In addition to NCEP, ECMWF and the Met Office have also reported positive forecast impacts due AIRS.

AIRS is a hyperspectral sounder with 2,378 infrared channels between 3.7 and 15.4 μm. NOAA/NESDIS routinely distributes AIRS data within 3 hours to NWP centers around the world. The AIRS design represents a breakthrough in infrared space instrumentation with measurement stability and accuracies far surpassing any current research or operational sounder..The results we describe in this paper are “work in progress,” and although significant accomplishments have already been made much more work remains in order to realize the full potential of this suite of instruments.

Abstract

In 2011, the National Oceanic and Atmospheric Administration (NOAA) began a cooperative initiative with the academic community to help address a vexing issue that has long been known as a disconnection between the operational and research realms for weather forecasting and data assimilation. The issue is the gap, more exotically referred to as the “valley of death,” between efforts within the broader research community and NOAA’s activities, which are heavily driven by operational constraints. With the stated goals of leveraging research community efforts to benefit NOAA’s mission and offering a path to operations for the latest research activities that support the NOAA mission, satellite data assimilation in particular, this initiative aims to enhance the linkage between NOAA’s operational systems and the research efforts. A critical component is the establishment of an efficient operations-to-research (O2R) environment on the Supercomputer for Satellite Simulations and Data Assimilation Studies (S4). This O2R environment is critical for successful research-to-operations (R2O) transitions because it allows rigorous tracking, implementation, and merging of any changes necessary (to operational software codes, scripts, libraries, etc.) to achieve the scientific enhancement. So far, the S4 O2R environment, with close to 4,700 computing cores (60 TFLOPs) and 1,700-TB disk storage capacity, has been a great success and consequently was recently expanded to significantly increase its computing capacity. The objective of this article is to highlight some of the major achievements and benefits of this O2R approach and some lessons learned, with the ultimate goal of inspiring other O2R/R2O initiatives in other areas and for other applications.