GOES digital imagery has been collected and processed using new techniques over portions of the United States since March 1988. High spatial and temporal resolution satellite cloud composite climatologies (SCCCs) have been produced that represent cloud frequency maps for each season. For each month studied, the cloud composite products represent the cloud occurrence frequency for each GOES pixel location and depict the overall spatial distribution of cloud cover over large portions of the United States.

The satellite composites present a new cloud climatology at a greater spatial and temporal resolution than previously available. Composites with ground resolutions of 2.5 km at hourly time intervals show striking patterns of cloud cover that are not detected in preexisting cloud climatologies.

A comparison between the SCCCs and climatologies produced from conventional surface observations is presented. The comparison is quite good for most stations, yet some significant differences are noted and discussed. Cloud occurrence in the vast areas between surface observing sites can now be analyzed using the new SCCC tool.

Abstract

Verification was performed on ensemble forecasts of 2009 Northern Hemisphere summer tropical cyclones (TCs) from two experimental global numerical weather prediction ensemble prediction systems (EPSs). The first model was a high-resolution version (T382L64) of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The second model was a 30-km version of the experimental NOAA/Earth System Research Laboratory’s Flow-following finite-volume Icosahedral Model (FIM). Both models were initialized with the first 20 members of a 60-member ensemble Kalman filter (EnKF) using the T382L64 GFS. The GFS–EnKF assimilated the full observational data stream that was normally assimilated into the NCEP operational Global Statistical Interpolation (GSI) data assimilation, plus human-synthesized “observations” of tropical cyclone central pressure and position produced at the National Hurricane Center and the Joint Typhoon Warning Center. The forecasts from the two experimental ensembles were compared against four operational EPSs from the European Centre for Medium-Range Weather Forecasts (ECMWF), NCEP, the Canadian Meteorological Centre (CMC), and the Met Office (UKMO).

The errors of GFS–EnKF ensemble track forecasts were competitive with those from the ECMWF ensemble system, and the overall spread of the ensemble tracks was consistent in magnitude with the track error. Both experimental EPSs had much lower errors than the operational NCEP, UKMO, and CMC EPSs, but the FIM–EnKF tracks were somewhat less accurate than the GFS–EnKF. The ensemble forecasts were often stretched in particular directions, and not necessarily along or across track. The better-performing EPSs provided useful information on potential track error anisotropy. While the GFS–EnKF initialized relatively deep vortices by assimilating the TC central pressure estimate, the model storms filled during the subsequent 24 h. Other forecast models also systematically underestimated TC intensity (e.g., maximum forecast surface wind speed). The higher-resolution models generally had less bias.

Analyses were conducted to try to understand whether the additional central pressure observation, the EnKF, or the extra resolution was most responsible for the decrease in track error of the experimental Global Ensemble Forecast System (GEFS)–EnKF over the operational NCEP. The assimilation of the additional TC observations produced only a small change in deterministic track forecasts initialized with the GSI. The T382L64 GFS–EnKF ensemble was used to initialize a T126L28 ensemble forecast to facilitate a comparison with the operational NCEP system. The T126L28 GFS–EnKF EPS track forecasts were dramatically better than the NCEP operational, suggesting the positive impact of the EnKF, perhaps through improved steering flow.

The Suomi National Polar-Orbiting Partnership (NPP) satellite was launched on 28 October 2011, heralding the next generation of operational U.S. polar-orbiting satellites. It carries the Visible– Infrared Imaging Radiometer Suite (VIIRS), a 22-band visible/infrared sensor that combines many of the best aspects of the NOAA Advanced Very High Resolution Radiometer (AVHRR), the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. VIIRS has nearly all the capabilities of MODIS, but offers a wider swath width (3,000 versus 2,330 km) and much higher spatial resolution at swath edge. VIIRS also has a day/night band (DNB) that is sensitive to very low levels of visible light at night such as those produced by moonlight reflecting off low clouds, fog, dust, ash plumes, and snow cover. In addition, VIIRS detects light emissions from cities, ships, oil flares, and lightning flashes.

NPP crosses the equator at about 0130 and 1330 local time, with VIIRS covering the entire Earth twice daily. Future members of the Joint Polar Satellite System (JPSS) constellation will also carry VIIRS. This paper presents dramatic early examples of multispectral VIIRS imagery capabilities and demonstrates basic applications of that imagery for a wide range of operational users, such as for fire detection, monitoring ice break up in rivers, and visualizing dust plumes over bright surfaces. VIIRS imagery, both single and multiband, as well as the day/night band, is shown to exceed both requirements and expectations.

Abstract

Long-term precipitation records are vital to many applications, especially the study of extreme events. The Tropical Rainfall Measuring Mission (TRMM) has served this need, but TRMM’s successor mission, Global Precipitation Measurement (GPM), does not yet provide a long-term record. Quantile mapping, the conversion of values across paired empirical distributions, offers a simple, established means to approximate such long-term statistics but only within appropriately defined domains. This method was applied to a case study in Central America, demonstrating that quantile mapping between TRMM and GPM data maintains the performance of a real-time landslide model. Use of quantile mapping could bring the benefits of the latest satellite-based precipitation dataset to existing user communities, such as those for hazard assessment, crop forecasting, numerical weather prediction, and disease tracking.

Abstract

During the summer of 2000, the Severe Thunderstorm Electrification and Precipitation Study (STEPS) program deployed a three-dimensional Lightning Mapping Array (LMA) near Goodland, Kansas. Video confirmation of sprites triggered by lightning within storms traversing the LMA domain were coordinated with extremely low frequency (ELF) transient measurements in Rhode Island and North Carolina. Two techniques of estimating changes in vertical charge moment (M _q ) yielded averages of ∼800 and ∼950 C km for 13 sprite-parent positive polarity cloud-to-ground strokes (+CGs). Analyses of the LMA's very high frequency (VHF) lightning emissions within the two mesoscale convective systems (MCSs) show that +CGs did not produce sprites until the centroid of the maximum density of lightning radiation emissions dropped from the upper part of the storm (7–11.5 km AGL) to much lower altitudes (2–5 km AGL). The average height of charge removal (Z _q ) from 15 sprite-parent +CGs during the late mature phase of one MCS was 4.1 km AGL. Thus, the total charges lowered by sprite-parent +CGs were on the order of 200 C. The regional 0°C isotherm was located at about 4.0 km AGL. This suggests a possible linkage between sprite-parent CGs and melting-layer/brightband charge production mechanisms in MCS stratiform precipitation regions. These cases are supportive of the conceptual MCS sprite-production models previously proposed by two of the authors (Lyons and Williams).

Abstract

Many existing models that predict landslide hazards utilize ground-based sources of precipitation data. In locations where ground-based precipitation observations are limited (i.e., a vast majority of the globe), or for landslide hazard models that assess regional or global domains, satellite multisensor precipitation products offer a promising near-real-time alternative to ground-based data. NASA’s global Landslide Hazard Assessment for Situational Awareness (LHASA) model uses the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) product to issue hazard “nowcasts” in near–real time for areas that are currently at risk for landsliding. Satellite-based precipitation estimates, however, can contain considerable systematic bias and random error, especially over mountainous terrain and during extreme rainfall events. This study combines a precipitation error modeling framework with a probabilistic adaptation of LHASA. Compared with the routine version of LHASA, this probabilistic version correctly predicts more of the observed landslides in the study region with fewer false alarms by high hazard nowcasts. This study demonstrates that improvements in landslide hazard prediction can be achieved regardless of whether the IMERG error model is trained using abundant ground-based precipitation observations or using far fewer and more scattered observations, suggesting that the approach is viable in data-limited regions. Results emphasize the importance of accounting for both random error and systematic satellite precipitation bias. The approach provides an example of how environmental prediction models can incorporate satellite precipitation uncertainty. Other applications such as flood and drought monitoring and forecasting could likely benefit from consideration of precipitation uncertainty.

Abstract

The lightning data that are recorded with a three-dimensional lightning mapping array (LMA) are compared with data from an electric field change sensor (in this case a flat-plate antenna operated both as a “slow” and a “fast” antenna). The goal of these comparisons is to quantify any time difference that may exist between the initial responses of the two instruments to a lightning flash. The data consist of 136 flashes from two New Mexico thunderstorms. It is found that the initial radiation source detected by the LMA usually precedes the initial response of both the slow and fast antennas. In a small number of cases, the flat-plate antenna response precedes the initial LMA source, but by no more than 2 ms. The observations of such a close time coincidence suggest that the first LMA radiation source of each flash was located at or very near the flash-initiation point. Thus, the first LMA radiation source and the initial sequence of sources from a lightning flash can be used as remote sensing tools to give information about the magnitude of the electric field (relative to lightning-initiation thresholds) and the direction of the electric field at the initiation location.

Abstract

A 3-h intermittent data assimilation system (Mesoscale Analysis and Prediction System—MAPS) configured in isentropic coordinates was developed and implemented in real-time operation. The major components of the system are data ingest, objective quality control of the observation, objective analysis, and a primitive equation forecast model, all using isentropic coordinates to take advantage of the improved resolution near frontal zones and greater spatial coherence of data that this coordinate system provides. Each 3-h forecast becomes the background for the subsequent analysis; in this manner, a four-dimensional set of observations can be assimilated.

The primary asynoptic data source used in current real-time operation of this system is air-craft data, most of it automated. Data from wind profilers, surface observations, and radiosondes are also included in MAPS.

Statistics were collected over the last half of 1989 and into 1990 to study the performance of MAPS and compare it with that of the Regional Analysis and Forecast System (RAFS), which is run operationally at the National Meteorological Center (NMC). Analyses generally fit mandatory-level observations more closely in MAPS than in RAFS. Three-hour forecasts from MAPS, incorporating asynoptic aircraft reports, improve on 12-h MAPS forecasts valid at the same time for all levels and variables, and also improve on 12-h RAFS forecasts of upper-level winds. This result is due to the quality and volume of the aircraft data as well as the effectiveness of the isentropic data assimilation used. Forecast fields at other levels are slightly poorer than those from RAFS. This may be largely due to the lack of diabatic and boundary-layer physics for the MAPS model used in this test period.

Abstract

Over the past 100 years, the collaborative effort of the international science community, including government weather services and the media, along with the associated proliferation of environmental observations, improved scientific understanding, and growth of technology, has radically transformed weather forecasting into an effective global and regional environmental prediction capability. This chapter traces the evolution of forecasting, starting in 1919 [when the American Meteorological Society (AMS) was founded], over four eras separated by breakpoints at 1939, 1956, and 1985. The current state of forecasting could not have been achieved without essential collaboration within and among countries in pursuing the common weather and Earth-system prediction challenge. AMS itself has had a strong role in enabling this international collaboration.

Abstract

The NOAA operational total precipitable water (TPW) anomaly product is available to forecasters to display percentage of normal TPW in real time for applications like heavy precipitation forecasts. In this work, the TPW anomaly is compared to multilayer cloud frequency and vertical structure. The hypothesis is tested that the TPW anomaly is reflective of changes in cloud vertical distribution, and that anomalously moist atmospheres have more and deeper clouds, while dry atmospheres have fewer and thinner clouds. Cloud vertical occurrence profiles from the CloudSat 94-GHz radar and the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) are composited according to TPW anomaly for summer and winter from 2007 to 2010. Three geographic regions are examined: the North Pacific (NPAC), the tropical east Pacific (Niño), and the Mississippi Valley (MSVL), which is a land-only region. Cloud likelihood increases as TPW anomaly values increase beyond 100% over MSVL and Niño. Over NPAC, shallow boundary layer cloud occurrence is not a function of TPW anomaly, while high clouds and deep clouds throughout the troposphere are more likely at higher TPW anomalies. In the Niño region, boundary layer clouds grow vertically as the TPW anomaly increases, and the anomaly range is smaller than in the midlatitudes. In summer, the MSVL region resembles Niño, but boundary layer clouds are observed less frequently than expected. The wintertime MSVL results do not show any compelling relationship, perhaps because of the difficulties in computing TPW anomaly in a very dry atmosphere.