ALBERT

Description: Aerosols are generated and transformed by myriad processes operating across many spatial and temporal scales. Evaluation of climate models and their sensitivity to changes, such as in greenhouse gas abundances, requires quantifying natural and anthropogenic aerosol forcings and accounting for other critical factors, such as cloud feedbacks. High accuracy is required to provide sufficient sensitivity to perturbations, separate anthropogenic from natural influences, and develop confidence in inputs used to support policy decisions. Although many relevant data sources exist, the aerosol research community does not currently have the means to combine these diverse inputs into an integrated data set for maximum scientific benefit. Bridging observational gaps, adapting to evolving measurements, and establishing rigorous protocols for evaluating models are necessary, while simultaneously maintaining consistent, well understood accuracies. The Progressive Aerosol Retrieval and Assimilation Global Observing Network (PARAGON) concept represents a systematic, integrated approach to global aerosol Characterization, bringing together modern measurement and modeling techniques, geospatial statistics methodologies, and high-performance information technologies to provide the machinery necessary for achieving a comprehensive understanding of how aerosol physical, chemical, and radiative processes impact the Earth system. We outline a framework for integrating and interpreting observations and models and establishing an accurate, consistent and cohesive long-term data record.

Description: Submillimeter-wave cloud ice radiometry is an innovative technique for determining the amount of ice present in cirrus clouds, measuring median crystal size, and constraining crystal shape. The radiometer described in this poster is being developed to acquire data to validate radiometric retrievals of cloud ice at submillimeter wavelengths. The goal of this effort is to develop a technique to enable spaceborne characterization of cirrus, meeting key climate modeling and NASA measurement needs.

Description: Atmospheric temperature measurement at three heights with thin wire thermocouples on the 1.1 m Mars Pathfinder meteorology must allow estimates of the integral scale of the atmospheric thermal turbulence during an 83 sol period that begins in the summer. The integral scale is a measure for regions of perturbations. In turbulent media that roughly characterizes locations where the perturbations are correlated. Excluding some to intervals with violent excursions of the mean temperatures, integral scale values are found that increase relatively rapidly from a few tenths meters or less near down to several meters by mid-morning. During mid-morning, the diurnal and shorter time scale wind direction variations often place the meteorology mast in the thermal wake of the Lander.

Description: High-resolution (approx. 40 km) along-track total column ozone data from the Total Ozone Mapping Spectrometer (TOMS) instrument are compared with a high-resolution mesoscale numerical model analysis of an intense cyclone in the Midwestern United States. Total ozone increased by 100 DU (nearly 38%) as the TOMS instrument passed over the associated tropopause fold region. Complex structure is seen in the meteorological fields and compares well with the total ozone observations. Ozone data support the meteorological analysis showing that stratospheric descent was confined to levels above approx. 600 hPa; significant positive potential vorticity at lower levels is attributable to diabetic processes. Likewise, meteorological fields show that two pronounced ozone streamers extending north and northeastward into Canada at high levels are not bands of stratospheric air feeding into the cyclone; one is a channel of exhaust downstream from the system, and the other apparently previously connected the main cyclonic circulation to a southward intrusion of polar stratospheric air and advected eastward as the cut-off cyclone evolved. Good agreement between small-scale features in the model output and total ozone data underscores the latter's potential usefulness in diagnosing upper tropospheric/lower stratospheric dynamics and kinematics.

Description: This report presents a new objective technique to verify predictions of the sea-breeze phenomenon over east-central Florida by the Regional Atmospheric Modeling System (RAMS) mesoscale numerical weather prediction (NWP) model. The Contour Error Map (CEM) technique identifies sea-breeze transition times in objectively-analyzed grids of observed and forecast wind, verifies the forecast sea-breeze transition times against the observed times, and computes the mean post-sea breeze wind direction and speed to compare the observed and forecast winds behind the sea-breeze front. The CEM technique is superior to traditional objective verification techniques and previously-used subjective verification methodologies because: It is automated, requiring little manual intervention, It accounts for both spatial and temporal scales and variations, It accurately identifies and verifies the sea-breeze transition times, and It provides verification contour maps and simple statistical parameters for easy interpretation. The CEM uses a parallel lowpass boxcar filter and a high-order bandpass filter to identify the sea-breeze transition times in the observed and model grid points. Once the transition times are identified, CEM fits a Gaussian histogram function to the actual histogram of transition time differences between the model and observations. The fitted parameters of the Gaussian function subsequently explain the timing bias and variance of the timing differences across the valid comparison domain. Once the transition times are all identified at each grid point, the CEM computes the mean wind direction and speed during the remainder of the day for all times and grid points after the sea-breeze transition time. The CEM technique performed quite well when compared to independent meteorological assessments of the sea-breeze transition times and results from a previously published subjective evaluation. The algorithm correctly identified a forecast or observed sea-breeze occurrence or absence 93% of the time during the two- month evaluation period from July and August 2000. Nearly all failures in CEM were the result of complex precipitation features (observed or forecast) that contaminated the wind field, resulting in a false identification of a sea-breeze transition. A qualitative comparison between the CEM timing errors and the subjectively determined observed and forecast transition times indicate that the algorithm performed very well overall. Most discrepancies between the CEM results and the subjective analysis were again caused by observed or forecast areas of precipitation that led to complex wind patterns. The CEM also failed on a day when the observed sea- breeze transition affected only a very small portion of the verification domain. Based on the results of CEM, the RAMS tended to predict the onset and movement of the sea-breeze transition too early and/or quickly. The domain-wide timing biases provided by CEM indicated an early bias on 30 out of 37 days when both an observed and forecast sea breeze occurred over the same portions of the analysis domain. These results are consistent with previous subjective verifications of the RAMS sea breeze predictions. A comparison of the mean post-sea breeze winds indicate that RAMS has a positive wind-speed bias for .all days, which is also consistent with the early bias in the sea-breeze transition time since the higher wind speeds resulted in a faster inland penetration of the sea breeze compared to reality.

Description: The Global Precipitation Climatology Project (GPCP) Version 2 Monthly Precipitation Analysis is described. This globally complete, monthly analysis of surface precipitation at 2.5 degrees x 2.5 degrees latitude-longitude resolution is available from January 1979 to the present. It is a merged analysis that incorporates precipitation estimates from low-orbit-satellite microwave data, geosynchronous-orbit-satellite infrared data, and rain gauge observations. The merging approach utilizes the higher accuracy of the low-orbit microwave observations to calibrate, or adjust, the more frequent geosynchronous infrared observations. The data set is extended back into the premicrowave era (before 1987) by using infrared-only observations calibrated to the microwave-based analysis of the later years. The combined satellite-based product is adjusted by the raingauge analysis. This monthly analysis is the foundation for the GPCP suite of products including those at finer temporal resolution, satellite estimate, and error estimates for each field. The 23-year GPCP climatology is characterized, along with time and space variations of precipitation.

Description: New state of the art methodology is described to analyze AIRS/AMSU/HSB data in the presence of multiple cloud formations. The methodology forms the basis for the AIRS Science Team algorithm which will be used to analyze AIRS/AMSU/HSB data on EOS Aqua. The cloud clearing methodology requires no knowledge of the spectral properties of the clouds. The basic retrieval methodology is general and extracts the maximum information from the radiances, consistent with the channel noise covariance matrix. The retrieval methodology minimizes the dependence of the solution on the first guess field and the first guess error characteristics. Results are shown for AIRS Science Team simulation studies with multiple cloud formations. These simulation studies imply that clear column radiances can be reconstructed under partial cloud cover with an accuracy comparable to single spot channel noise in the temperature and water vapor sounding regions, temperature soundings can be produced under partial cloud cover with RMS errors on the order of, or better than, 1deg K in 1 km thick layers from the surface to 700 mb, 1 km layers from 700 mb to 300 mb, 3 km layers from 300 mb to 30 mb, and 5 km layers from 30 mb to 1 mb, and moisture profiles can be obtained with an accuracy better than 20% absolute errors in 1 km layers from the surface to nearly 200 mb.

Description: There has been increasing effort in recent years to employ satellite remotely sensed data to identify and map vector habitat and malaria transmission risk in data sparse environments. In the current investigation, available satellite and other land surface climatology data products are employed in short-term forecasting of infection rates in the Mpumalanga Province of South Africa, using a multivariate autoregressive approach. The climatology variables include precipitation, air temperature and other land surface states computed by the Off-line Land-Surface Global Assimilation System (OLGA) including soil moisture and surface evaporation. Satellite data products include the Normalized Difference Vegetation Index (NDVI) and other forcing data used in the Goddard Earth Observing System (GEOS-1) model. Predictions are compared to long- term monthly records of clinical and microscopic diagnoses. The approach addresses the high degree of short-term autocorrelation in the disease and weather time series. The resulting model is able to predict 11 of the 13 months that were classified as high risk during the validation period, indicating the utility of applying antecedent climatic variables to the prediction of malaria incidence for the Mpumalanga Province.

Description: Conventional wisdom is that lidar pulses do not significantly penetrate clouds having optical thickness exceeding about tau = 2, and that no returns are detectable from more than a shallow skin depth. Yet optically thicker clouds of tau much greater than 2 reflect a larger fraction of visible photons, and account for much of Earth s global average albedo. As cloud layer thickness grows, an increasing fraction of reflected photons are scattered multiple times within the cloud, and return from a diffuse concentric halo that grows around the incident pulse, increasing in horizontal area with layer physical thickness. The reflected halo is largely undetected by narrow field-of-view (FoV) receivers commonly used in lidar applications. THOR - Thickness from Off-beam Returns - is an airborne wide-angle detection system with multiple FoVs, capable of observing the diffuse halo, detecting wide-angle signal from which physical thickness of optically thick clouds can be retrieved. In this paper we describe the THOR system, demonstrate that the halo signal is stronger for thicker clouds, and validate physical thickness retrievals for clouds having z 〉 20, from NASA P-3B flights over the Department of Energy/Atmospheric Radiation Measurement/Southern Great Plains site, using the lidar, radar and other ancillary ground-based data.

Description: We report results from a study with the Numerical Spectral Model (NSM), which produces in the mesosphere significant inter-annual variations in the diurnal tide. Applying Hines Doppler Spread Parameterization (DPS), small-scale gravity waves (GW) drive the Quasi-biennial Oscillation (QBO) and Semi-annual Oscillation (SAO). With a GW source that peaks at the equator and is taken to be isotropic and independent of season, the NSM generates near the equator a QBO with variable periods around 27 months and zonal wind amplitudes close to 20 m / s at 30 Ism. As reported earlier, the NSM reproduces the observed equinoctial maxima in the diurnal tide at altitudes around 95 km. In the present paper it is shown that the QBO modulates the tide such that the seasonal amplitude maxima can vary from one year to another by as much as 30%. Since the period of the QBO is variable, its phase relative to the seasonal cycle changes. The magnitude of the QBO modulation of the tide thus varies considerably as our long-term model simulation shows. To shed light on the underlying mechanism, the relative importance of the linearized advection terms are discussed that involve the meridional and vertical winds of the diurnal tide.