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Thomas P. Charlock
and
William L. Smith Jr.
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P. L. Smith Jr.
,
C. G. Myers
, and
H. D. Orville

Abstract

This paper describes and compares various methods for calculating radar reflectivity factors in numerical cloud models that use bulk methods to characterize the precipitation processes. Equations sensitive to changes in the parameters of the particle size distributions are favored because they allow simulation of phenomena causing such changes. Marshall-Palmer-type functions are established to represent hailstone size distributions because the previously available distributions lead to implausibly large reflectivity factors. Simplified equations are developed for calculating reflectivity factors for both dry and wet hail. Some examples are given of the use of the various equations in numerical cloud models.

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A. S. Dennis
,
P. L. Smith Jr.
,
G. A. P. Peterson
, and
R. D. McNeil

Abstract

An electronic hailstone momentum sensor has been developed which records hailstone impacts on magnetic tape. The instrument and the programs for analyzing the recorded data are described. Sensors were operated during six hailstorms in 1969 and recorded 624 hailstone impacts. Derived frequency distributions of hailstone size show the median diameter for all stones recorded to be near 0.9 cm. Significant variations exist among storms. Values of the equivalent radar reflectivity factor Z e have been computed for the hailshafts sampled, for X-hand and S-band radar, and for both wet and dry hailstones. The values range up to 70 dBz (107.0 mm6 m−3) and show good agreement with radar-measured values. The results suggest that dual-wavelength radar systems are not much better than S-band sets alone for estimating hailstone size.

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D. J. Musil
,
E. L. May
,
P. L. Smith Jr.
, and
W. R. Sand

Abstract

Precipitation particle sizes were measured using a continuous hydrometeor sampler (foil impactor) during penetrations of hailstorms with an armored T-28 aircraft. Data have been analyzed from three penetrations of a storm near Raymer, Colorado, on 9 July 1973 at altitudes between 5.5 and 7.2 km MSL, which correspond to temperatures between about −2°C and −12°C. Other results pertinent to the Raymer storm are discussed in Parts I,II,III and elsewhere in this issue.

Most of the particles were identified as ice particles or ones containing both ice and water; however, significant amounts of liquid particles were found in the updrafts of developing cells at temperatures as cold as −12°C. Particles larger than 5 mm in diameter were typically found along the edges of the updrafts, with the precipitation concentrations being strongly dependent on these larger particles. The downdrafts were composed of ice particles.

Several particle size distributions from one of the penetrations were examined. The distributions are roughly exponential, or bi-exponential when large particles are present.

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A. S. Dennis
,
Alexander Koscielski
,
D. E. Cain
,
J. H. Hirsch
, and
P. L. Smith Jr.

Abstract

Magnetic tape records for radar observations of 80 moving one-hour test cases in a three-way randomized (no-seed, salt, silver iodide) cloud seeding experiment have been analyzed in terms of echoing areas and radar-estimated rainfall amounts. Individual test cases ranged from non-precipitating cumulus up to moderate thunderstorms with echoing areas exceeding 100 km2 and rainfall estimated at 3000 kT in 1 h.Out of numerous predictor variables, cloud depth is found to be the best single predictor for both echoing area and radar-estimated rainfall. The echoing area and radar-estimated rainfall are very closely correlated. A cube-root transformation of the radar-estimated rainfall improves the correlation between cloud depth and the radar-estimated rainfall for the no-seed (control) sample to 0.91. For clouds of a given depth, both the echoing area and radar-estimated rainfall are larger in seeded than in unseeded cases. The differences between no-seed and salt cases are of marginal statistical significance, but the differences in echoing area and rainfall between no-seed and silver iodide cases are significant at the 1% level. The indicated effects, expressed as a percentage of the echoing area or radar-estimated rainfall in the no-seed cases, decrease with cloud depth.A comparison of no-seed and AgI cases with the aid of a one-dimensional steady-state cloud model shows that AgI seeding may have led to increases in maximum cloud height averaging 600 m.It is concluded that seeding affected the precipitation in the Cloud Catcher test cases through both the microphysical processes and the cloud dynamics.

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W. L. Smith Jr.
,
T. P. Charlock
,
R. Kahn
,
J. V. Martins
,
L. A. Remer
,
P. V. Hobbs
,
J. Redemann
, and
C. K. Rutledge

Abstract

NASA developed an Earth Observing System (EOS) to study global change and reduce uncertainties associated with aerosols and other key parameters controlling climate. The first EOS satellite, Terra, was launched in December 1999. The Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS) field campaign was conducted from 10 July to 2 August 2001 to validate several Terra data products, including aerosol properties and radiative flux profiles derived from three complementary Terra instruments: the Clouds and the Earth’s Radiant Energy System (CERES), the Multiangle Imaging Spectroradiometer (MISR), and the Moderate Resolution Imaging Spectroradiometer (MODIS). CERES, MISR, and MODIS are being used to investigate the critical role aerosols play in modulating the radiative heat budget of the earth–atmosphere system. CLAMS’ primary objectives are to improve understanding of atmospheric aerosols, to validate and improve the satellite data products, and to test new instruments and measurement concepts. A variety of in situ sampling devices and passive remote sensing instruments were flown on six aircraft to characterize the state of the atmosphere, the composition of atmospheric aerosols, and the associated surface and atmospheric radiation parameters over the U.S. eastern seaboard. Aerosol particulate matter was measured at two ground stations established at Wallops Island, Virginia, and the Chesapeake Lighthouse, the site of an ongoing CERES Ocean Validation Experiment (COVE) where well-calibrated radiative fluxes and Aerosol Robotic Network (AERONET) aerosol properties have been measured since 1999. Nine coordinated aircraft missions and numerous additional sorties were flown under a variety of atmospheric conditions and aerosol loadings. On one “golden day” (17 July 2001), under moderately polluted conditions with midvisible optical depths near 0.5, all six aircraft flew coordinated patterns vertically stacked between 100 and 65 000 ft over the COVE site as Terra flew overhead. This overview presents a description of CLAMS objectives, measurements, and sampling strategies. Key results, reported in greater detail in the collection of papers found in this special issue, are also summarized.

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Charles K. Rutledge
,
Gregory L. Schuster
,
Thomas P. Charlock
,
Frederick M. Denn
,
William L. Smith Jr.
,
Bryan E. Fabbri
,
James J. Madigan Jr.
, and
Robert J. Knapp

When radiometers on satellites point toward Earth with the goal of sensing an important variable quantitatively, rather than just creating a pleasing image, the task at hand is often not simple. The electromagnetic energy detected by the radiometers is a puzzle of various signals; it must be solved to quantify the specific physical variable. This task, called the retrieval or remote-sensing process, is important to most satellite-based observation programs. It would be ideal to test the algorithms for retrieval processes in a sealed laboratory, where all the relevant parameters could be easily measured. The size and complexity of the Earth make this impractical. NASA's Clouds and the Earth's Radiant Energy System (CERES) project has done the next-best thing by developing a long-term radiation observation site over the ocean. The relatively low and homogeneous surface albedo of the ocean make this type of site a simpler environment for observing and validating radiation parameters from satellite-based instruments. To characterize components of the planet's energy budget, CERES uses a variety of retrievals associated with several satellite-based instruments onboard NASA's Earth Observing System (EOS). A new surface observation project called the CERES Ocean Validation Experiment (COVE), operating on a rigid ocean platform, is supplying data to validate some of these instruments and retrieval products. This article describes the ocean platform and the types of observations being performed there, and highlights of some scientific problems being addressed.

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J. Redemann
,
B. Schmid
,
J. A. Eilers
,
R. Kahn
,
R. C. Levy
,
P. B. Russell
,
J. M. Livingston
,
P. V. Hobbs
,
W. L. Smith Jr.
, and
B. N. Holben

Abstract

As part of the Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS) experiment, 10 July–2 August 2001, off the central East Coast of the United States, the 14-channel NASA Ames Airborne Tracking Sunphotometer (AATS-14) was operated aboard the University of Washington’s Convair 580 (CV-580) research aircraft during 10 flights (∼45 flight hours). One of the main research goals in CLAMS was the validation of satellite-based retrievals of aerosol properties. The goal of this study in particular was to perform true over-ocean validations (rather than over-ocean validation with ground-based, coastal sites) at finer spatial scales and extending to longer wavelengths than those considered in previous studies. Comparisons of aerosol optical depth (AOD) between the Aerosol Robotic Network (AERONET) Cimel instrument at the Chesapeake Lighthouse and airborne measurements by AATS-14 in its vicinity showed good agreement with the largest r-square correlation coefficients at wavelengths of 0.38 and 0.5 μm (>0.99). Coordinated low-level flight tracks of the CV-580 during Terra overpass times permitted validation of over-ocean Moderate Resolution Imaging Spectroradiometer (MODIS) level 2 (MOD04_L2) multiwavelength AOD data (10 km × 10 km, nadir) in 16 cases on three separate days. While the correlation between AATS-14- and MODIS-derived AOD was weak with an r square of 0.55, almost 75% of all MODIS AOD measurements fell within the prelaunch estimated uncertainty range Δτ = ±0.03 ± 0.05τ. This weak correlation may be due to the small AODs (generally less than 0.1 at 0.5 μm) encountered in these comparison cases. An analogous coordination exercise resulted in seven coincident over-ocean matchups between AATS-14 and Multiangle Imaging Spectroradiometer (MISR) measurements. The comparison between AATS-14 and the MISR standard algorithm regional mean AODs showed a stronger correlation with an r square of 0.94. However, MISR AODs were systematically larger than the corresponding AATS values, with an rms difference of ∼0.06. AATS data collected during nine extended low-level CV-580 flight tracks were used to assess the spatial variability in AOD at horizontal scales up to 100 km. At UV and midvisible wavelengths, the largest absolute gradients in AOD were 0.1–0.2 per 50-km horizontal distance. In the near-IR, analogous gradients rarely reached 0.05. On any given day, the relative gradients in AOD were remarkably similar for all wavelengths, with maximum values of 70% (50 km)−1 and more typical values of 25% (50 km)−1. The implications of these unique measurements of AOD spatial variability for common validation practices of satellite data products and for comparisons to large-scale aerosol models are discussed.

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Stanley G. Benjamin
,
Eric P. James
,
Ming Hu
,
Curtis R. Alexander
,
Therese T. Ladwig
,
John M. Brown
,
Stephen S. Weygandt
,
David D. Turner
,
Patrick Minnis
,
William L. Smith Jr.
, and
Andrew K. Heidinger

Abstract

Accurate cloud and precipitation forecasts are a fundamental component of short-range data assimilation/model prediction systems such as the NOAA 3-km High-Resolution Rapid Refresh (HRRR) or the 13-km Rapid Refresh (RAP). To reduce cloud and precipitation spinup problems, a nonvariational assimilation technique for stratiform clouds was developed within the Gridpoint Statistical Interpolation (GSI) data assimilation system. One goal of this technique is retention of observed stratiform cloudy and clear 3D volumes into the subsequent model forecast. The cloud observations used include cloud-top data from satellite brightness temperatures, surface-based ceilometer data, and surface visibility. Quality control, expansion into spatial information content, and forward operators are described for each observation type. The projection of data from these observation types into an observation-based cloud-information 3D gridded field is accomplished via identification of cloudy, clear, and cloud-unknown 3D volumes. Updating of forecast background fields is accomplished through clearing and building of cloud water and cloud ice with associated modifications to water vapor and temperature. Impact of the cloud assimilation on short-range forecasts is assessed with a set of retrospective experiments in warm and cold seasons using the RAPv5 model. Short-range (1–9 h) forecast skill is improved in both seasons for cloud ceiling and visibility and for 2-m temperature in daytime and with mixed results for other measures. Two modifications were introduced and tested with success: use of prognostic subgrid-scale cloud fraction to condition cloud building (in response to a high bias) and removal of a WRF-based rebalancing.

Open access
Christopher D. Karstens
,
James Correia Jr.
,
Daphne S. LaDue
,
Jonathan Wolfe
,
Tiffany C. Meyer
,
David R. Harrison
,
John L. Cintineo
,
Kristin M. Calhoun
,
Travis M. Smith
,
Alan E. Gerard
, and
Lans P. Rothfusz

Abstract

Providing advance warning for impending severe convective weather events (i.e., tornadoes, hail, wind) fundamentally requires an ability to predict and/or detect these hazards and subsequently communicate their potential threat in real time. The National Weather Service (NWS) provides advance warning for severe convective weather through the issuance of tornado and severe thunderstorm warnings, a system that has remained relatively unchanged for approximately the past 65 years. Forecasting a Continuum of Environmental Threats (FACETs) proposes a reinvention of this system, transitioning from a deterministic product-centric paradigm to one based on probabilistic hazard information (PHI) for hazardous weather events. Four years of iterative development and rapid prototyping in the National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) with NWS forecasters and partners has yielded insights into this new paradigm by discovering efficient ways to generate, inform, and utilize a continuous flow of information through the development of a human–machine mix. Forecasters conditionally used automated object-based guidance within four levels of automation to issue deterministic products containing PHI. Forecasters accomplished this task in a timely manner while focusing on communication and conveying forecast confidence, elements considered necessary by emergency managers. Observed annual increases in the usage of first-guess probabilistic guidance by forecasters were related to improvements made to the prototyped software, guidance, and techniques. However, increasing usage of automation requires improvements in guidance, data integration, and data visualization to garner trust more effectively. Additional opportunities exist to address limitations in procedures for motion derivation and geospatial mapping of subjective probability.

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