Stratospheric temperatures : vertical resolution of retrieved profiles

This work describes the problem of retrieval of temperature profiles from Nimbus 4 Selective Chopper Radiometer (SCR) measurements of emission from the 15µm CO₂ absorption band. Vertical resolution diagnostics are discussed for : (a) an optimum retrieval estimator which uses a priori statistical information (the maximum likelihood or maximum a posteriori estimator), and (b) a Backus Gilbert estimator which uses essentially no a priori information. Confidence regions are calculated for the optimum retrieval estimator. At stratospheric heights, and for Nimbus 4 SCR data, the resolving power of the optimum estimator is poorer than that of the satellite observations and is sensitive to the size of the measurement noise, and the choice of first guess profile. The retrieval from the optimum estimator is unstable when detecting vertical wave-like disturbances on temperature profiles. It is suggested that retrieval estimators using a priori statistical information should not be used when trying to detect vertical wave-like structure in the atmosphere

Stratospheric Gravity Wave Fluxes and Scales during DEEPWAVE

During the Deep Propagating Gravity Wave Experiment (DEEPWAVE) project in June and July 2014, the Gulfstream V research aircraft flew 97 legs over the Southern Alps of New Zealand and 150 legs over the Tasman Sea and Southern Ocean, mostly in the low stratosphere at 12.1-km altitude. Improved instrument calibration, redundant sensors, longer flight legs, energy flux estimation, and scale analysis revealed several new gravity wave properties. Over the sea, flight-level wave fluxes mostly fell below the detection threshold. Over terrain, disturbances had characteristic mountain wave attributes of positive vertical energy flux (EFz), negative zonal momentum flux, and upwind horizontal energy flux. In some cases, the fluxes changed rapidly within an 8-h flight, even though environmental conditions were nearly unchanged. The largest observed zonal momentum and vertical energy fluxes were MFx = −550 mPa and EFz = 22 W m−2, respectively. A wide variety of disturbance scales were found at flight level over New Zealand. The vertical wind variance at flight level was dominated by short “fluxless” waves with wavelengths in the 6–15-km range. Even shorter scales, down to 500 m, were found in wave breaking regions. The wavelength of the flux-carrying mountain waves was much longer—mostly between 60 and 150 km. In the strong cases, however, with EFz \u3e 4 W m−2, the dominant flux wavelength decreased (i.e., “downshifted”) to an intermediate wavelength between 20 and 60 km. A potential explanation for the rapid flux changes and the scale “downshifting” is that low-level flow can shift between “terrain following” and “envelope following” associated with trapped air in steep New Zealand valleys

Data Assimilation Enhancements to Air Force Weathers Land Information System

The United States Air Force (USAF) has a proud and storied tradition of enabling significant advancements in the area of characterizing and modeling land state information. 557th Weather Wing (557 WW; DoDs Executive Agent for Land Information) provides routine geospatial intelligence information to warfighters, planners, and decision makers at all echelons and services of the U.S. military, government and intelligence community. 557 WW and its predecessors have been home to the DoDs only operational regional and global land data analysis systems since January 1958. As a trusted partner since 2005, Air Force Weather (AFW) has relied on the Hydrological Sciences Laboratory at NASA/GSFC to lead the interagency scientific collaboration known as the Land Information System (LIS). LIS is an advanced software framework for high performance land surface modeling and data assimilation of geospatial intelligence (GEOINT) information

The Deep Propagating Gravity Wave Experiment (DEEPWAVE): An airborne and ground-based exploration of gravity wave propagation and effects from their sources throughout the lower and middle atmosphere

The Deep Propagating Gravity Wave Experiment (DEEPWAVE) was designed to quantify gravity wave (GW) dynamics and effects from orographic and other sources to regions of dissipation at high altitudes. The core DEEPWAVE field phase took place from May through July 2014 using a comprehensive suite of airborne and ground-based instruments providing measurements from Earth’s surface to ∼100 km. Austral winter was chosen to observe deep GW propagation to high altitudes. DEEPWAVE was based on South Island, New Zealand, to provide access to the New Zealand and Tasmanian “hotspots” of GW activity and additional GW sources over the Southern Ocean and Tasman Sea. To observe GWs up to ∼100 km, DEEPWAVE utilized three new instruments built specifically for the National Science Foundation (NSF)/National Center for Atmospheric Research (NCAR) Gulfstream V (GV): a Rayleigh lidar, a sodium resonance lidar, and an advanced mesosphere temperature mapper. These measurements were supplemented by in situ probes, dropsondes, and a microwave temperature profiler on the GV and by in situ probes and a Doppler lidar aboard the German DLR Falcon. Extensive ground-based instrumentation and radiosondes were deployed on South Island, Tasmania, and Southern Ocean islands. Deep orographic GWs were a primary target but multiple flights also observed deep GWs arising from deep convection, jet streams, and frontal systems. Highlights include the following: 1) strong orographic GW forcing accompanying strong cross-mountain flows, 2) strong high-altitude responses even when orographic forcing was weak, 3) large-scale GWs at high altitudes arising from jet stream sources, and 4) significant flight-level energy fluxes and often very large momentum fluxes at high altitudes

Prediction of Moderate and Heavy Rainfall in New Zealand Using Data Assimilation and Ensemble

This numerical weather prediction study investigates the effects of data assimilation and ensemble prediction on the forecast accuracy of moderate and heavy rainfall over New Zealand. In order to ascertain the optimal implementation of state-of-the-art 3Dvar and 4Dvar data assimilation techniques, 12 different experiments have been conducted for the period from 13 September to 18 October 2010 using the New Zealand limited area model. Verification has shown that an ensemble based on these experiments outperforms all of the individual members using a variety of metrics. In addition, the rainfall occurrence probability derived from the ensemble is a good predictor of heavy rainfall. Mountains significantly affect the performance of this ensemble which provides better forecasts of heavy rainfall over the South Island than over the North Island. Analysis suggests that underestimation of orographic lifting due to the relatively low resolution of the model (~12 km) is a factor leading to this variability in heavy rainfall forecast skill. This study indicates that regional ensemble prediction with a suitably fine model resolution (≤5 km) would be a useful tool for forecasting heavy rainfall over New Zealand

Improving the Usefulness of Operational Radiosonde Data

The Workshop to Improve the Usefulness of Operational Radiosonde Data was held at the NOAA National Climatic Data Center (NCDC) in Asheville, North Carolina, from 11 through 13 March 2003. It brought together users of global radiosonde data in numerical weather prediction, climate, and satellite data applications, along with a number of experts concerned with radiosonde instrument development, validation, and operational programs. This report provides a set of findings and recommendations produced by the group. The recommendations address issues in the areas of accuracy, calibration, and corrections of radiosonde measurements, sampling strategies, and the exchange of and response to information on data integrity, metadata, and data processing strategies

Tuna Schools/aggregations in Surface Longline Data 1993--98

Exploratory data analysis of a highresolution (hook-by-hook), 6-year time series (1993-- 98) of observed longline catch data for tunas was used to investigate fine-scale spatial patterns along individual sets that may be indicative of social behaviour (i.e., schooling) and/or the response of individual fish to favourable extrinsic conditions (i.e., aggregation). Methods of spatial data analysis (i.e., nearest neighbour analysis) that have previously been applied in various other sciences (e.g., forestry and astronomy) were used. Results indicate strong clustering of individual tunas at characteristic scales within the set. Mean Nearest Neighbour Distances (NNDs) were between 100 and 200 m, compared with NNDs of 200--700 m predicted by a heterogeneous Poisson process on the same spatial domain. The results suggest that these adult tunas were either schooling or aggregating at the time of capture; this may therefore be related either to social behaviour or to sub-mesoscale oceanographic features. An aggregation index was derived from the NNDs, giving a classification method that may be used for similar data and the development of empirical models attempting to relate patterns in fish catch distributions to environmental variables. The success of such models will ultimately depend on elucidating the ecological processes reflected in oceanographic features at biologically meaningful spatial scales