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At least 361 records · Page 20

A Study of Wake Development and Structure in Constant Pressure Gradients

Motivated by the application to high-lift aerodynamics for commercial transport aircraft, a systematic investigation into the response of symmetric/asymmetric planar turbulent wake development to constant adverse, zero, and favorable pressure gradients has been conducted. The experiments are performed at a Reynolds number of 2.4 million based on the chord of the wake generator. A unique feature of this wake study is that the pressure gradients imposed on the wake flow field are held constant. The experimental measurements involve both conventional LDV and hot wire flow field surveys of mean and turbulent quantities including the turbulent kinetic energy budget. In addition, similarity analysis and numerical simulation have also been conducted for this wake study. A focus of the research has been to isolate the effects of both pressure gradient and initial wake asymmetry on the wake development. Experimental results reveal that the pressure gradient has a tremendous influence on the wake development, despite the relatively modest pressure gradients imposed. For a given pressure gradient, the development of an initially asymmetric wake is different from the initially symmetric wake. An explicit similarity solution for the shape parameters of the symmetric wake is obtained and agrees with the experimental results. The turbulent kinetic energy budget measurements of the symmetric wake demonstrate that except for the convection term, the imposed pressure gradient does not change the fundamental flow physics of turbulent kinetic energy transport. Based on the turbulent kinetic energy budget measurements, an approach to correct the bias error associated with the notoriously difficult dissipation estimate is proposed and validated through the comparison of the experimental estimate with a direct numerical simulation result.

Thomas, Flint O.↗

The Effect of Errors in Snow Assimilation on Land Surface Modeling

The accurate portrayal of the hydrological cycle is extremely important in land surface modeling. Central to this effort is the treatment of snow, as errors in the representation of this quantity can impact practically all other modeled quantities through alterations in the water and energy balances. Although land surface model (LSM) simulations can benefit from the assimilation of snow cover and snow depth observations, they can be negatively impacted if such observations contain errors or if a model bias exists in the simulation of surface or soil temperatures. Both cases may lead to excessive melting or growth of snow packs, and to large alterations in both the energy and water balances. Such problems in the snow assimilation process, made evident by the repeated melting and replenishing of snow pack over significant areas of the United States, exists in the Eta Data Assimilation System and is a product of the EDAS system's direct insertion assimilation of snow data. Occurring on a 24 hour cycle, the repeated melting infuses the soil column with a large quantity of water that upsets the hydrological cycle. In an effort to quantify the impacts of such errors in snow assimilation on water and energy budgets, a series of Mosaic LSM simulations were performed over the 12 month period covering October 1998 to October 1999.

Cosgrove, Brian A.↗

The Global Energy Balance of Titan

We report the first measurement of the global emitted power of Titan. Longterm (2004-2010) observations conducted by the Composite Infrared Spectrometer (CIRS) onboard Cassini reveal that the total emitted power by Titan is (2.84 plus or minus 0.01) x 10(exp 8) watts. Together with previous measurements of the global absorbed solar power of Titan, the CIRS measurements indicate that the global energy budget of Titan is in equilibrium within measurement error. The uncertainty in the absorbed solar energy places an upper limit on the energy imbalance of 5.3%.

Li, Liming↗

Uncertainties in Coastal Ocean Color Products: Impacts of Spatial Sampling

With increasing demands for ocean color (OC) products with improved accuracy and well characterized, per-retrieval uncertainty budgets, it is vital to decompose overall estimated errors into their primary components. Amongst various contributing elements (e.g., instrument calibration, atmospheric correction, inversion algorithms) in the uncertainty of an OC observation, less attention has been paid to uncertainties associated with spatial sampling. In this paper, we simulate MODIS (aboard both Aqua and Terra) and VIIRS OC products using 30 m resolution OC products derived from the Operational Land Imager (OLI) aboard Landsat-8, to examine impacts of spatial sampling on both cross-sensor product intercomparisons and in-situ validations of R(sub rs) products in coastal waters. Various OLI OC products representing different productivity levels and in-water spatial features were scanned for one full orbital-repeat cycle of each ocean color satellite. While some view-angle dependent differences in simulated Aqua-MODIS and VIIRS were observed, the average uncertainties (absolute) in product intercomparisons (due to differences in spatial sampling) at regional scales are found to be 1.8%, 1.9%, 2.4%, 4.3%, 2.7%, 1.8%, and 4% for the R(sub rs)(443), R(sub rs)(482), R(sub rs)(561), R(sub rs)(655), Chla, K(sub d)(482), and b(sub bp)(655) products, respectively. It is also found that, depending on in-water spatial variability and the sensor's footprint size, the errors for an in-situ validation station in coastal areas can reach as high as +/- 18%. We conclude that a) expected biases induced by the spatial sampling in product intercomparisons are mitigated when products are averaged over at least 7 km × 7 km areas, b) VIIRS observations, with improved consistency in cross-track spatial sampling, yield more precise calibration/validation statistics than that of MODIS, and c) use of a single pixel centered on in-situ coastal stations provides an optimal sampling size for validation efforts. These findings will have implications for enhancing our understanding of uncertainties in ocean color retrievals and for planning of future ocean color missions and the associated calibration/validation exercises.

Coastal ocean color↗

Assessment of Satellite Surface Radiation Products in Highland Regions with Tibet Instrumental Data

This study presents results of comparisons between instrumental radiation data in the elevated Tibetan Plateau and two global satellite products: the Global Energy and Water Cycle Experiment - Surface Radiation Budget (GEWEX-SRB) and International Satellite Cloud Climatology Project - Flux Data (ISCCP-FD). In general, shortwave radiation (SW) is estimated better by ISCCP-FD while longwave radiation (LW) is estimated better by GEWEX-SRB, but all the radiation components in both products are under-estimated. Severe and systematic errors were found in monthly-mean SRB SW (on plateau-average, -48 W/sq m for downward SW and -18 W/sq m for upward SW) and FD LW (on plateau-average, -37 W/sq m for downward LW and -62 W/sq m for upward LW) for radiation. Errors in monthly-mean diurnal variations are even larger than the monthly mean errors. Though the LW errors can be reduced about 10 W/sq m after a correction for altitude difference between the site and SRB and FD grids, these errors are still higher than that for other regions. The large errors in SRB SW was mainly due to a processing mistake for elevation effect, but the errors in SRB LW was mainly due to significant errors in input data. We suggest reprocessing satellite surface radiation budget data, at least for highland areas like Tibet.

Yang, Kun↗

MIC-DP: A Scalable Correlation-Aware Differential Privacy Framework for High-Dimensional Data

Conventional differential privacy (DP) assumes record independence, limiting effectiveness on real-world datasets with temporal, spatial, or structural correlations. These dependencies undermine privacy guarantees and degrade utility in domains like healthcare, IoT, and smart city analytics. We propose Maximum Information Correlated Differential Privacy (MIC-DP), a novel framework that dynamically calibrates noise based on statistical dependencies. MIC-DP uses the Maximum Information Coefficient (MIC) to capture both linear and nonlinear correlations without explicit modeling, enabling adaptive sensitivity adjustment and improved privacy–utility trade-offs. Evaluations on healthcare (MIMIC), demographic (ACI), and synthetic datasets show that MIC-DP reduces mean absolute error (MAE) by up to 5.2% under strict privacy budgets (ϵ≤1), with aggregate utility improvements reaching 18% across datasets and evaluation metrics. MIC-DP provides formal (ϵ,δ)-privacy guarantees, scales efficiently with feature count, and supports deployment in moderate-scale, privacy-sensitive applications. Its tunable performance and runtime efficiency make MIC-DP suitable for privacy-sensitive applications where low-latency analytics and strong privacy guarantees must coexist. These results demonstrate MIC-DP’s effectiveness as a correlation-aware solution for practical DP.

Yang, Wenjun [Univ. of Washington, Tacoma, WA (Uni↗

Calibrating CERES and VIIRS using CLARREO Pathfinder: Adjustment for the Viewing Geometry Mismatch

The Climate Absolute Radiance and Refractivity Observatory Pathfinder (CPF) mission on the International Space Station (ISS) will provide, for the first-time, an SI-traceable on-orbit calibration reference with a reflectance uncertainty of 0.3% (1 sigma). One major mission objective is to demonstrate the capability of transferring CPF’s radiometric accuracy to other satellite-based instruments, such as Clouds and Earth's Radiant Energy System (CERES) and Visible Infrared Imaging Radiometer Suite (VIIRS) on NOAA-20. CPF inter-calibration measurements will be planned to obtain inter-calibration samples that closely match satellite-based target sensor observations in time, space, angle, and wavelength. The inter-calibration event observations cover a wide range of viewing geometry angles with potential angular mismatch between CPF and the target sensors which can introduce non-negligible errors against the mission’s inter-calibration uncertainty budget. We will introduce the CPF approach to adjust for angular mismatch differences, thereby reducing the incurred uncertainties to within the inter-calibration uncertainty budget. The angular adjustment algorithm has been developed and validated based on the high-fidelity simulations of the CPF spectra using the Principal Component based Radiative Transfer Model (PCRTM). The implementation of the algorithm on event simulation data and the characterization for the angular adjustment uncertainty will be presented.

Wan Wu↗

More buck-per-shot: Why learning trumps mitigation in noisy quantum sensing

Quantum sensing is one of the most promising applications for quantum technologies. However, reaching the ultimate sensitivities enabled by the laws of quantum mechanics can be a challenging task in realistic scenarios where noise is present. While several strategies have been proposed to deal with the detrimental effects of noise, these come at the cost of an extra shot budget. Given that shots are a precious resource for sensing – as infinite measurements could lead to infinite precision – care must be taken to truly guarantee that any shot not being used for sensing is actually leading to some metrological improvement. In this work, we study whether investing shots in error-mitigation, inference techniques, or combinations thereof, can improve the sensitivity of a noisy quantum sensor on a (shot) budget. We present a detailed bias–variance error analysis for various sensing protocols. Our results show that the costs of zero-noise extrapolation techniques outweigh their benefits. We also find that pre-characterizing a quantum sensor via inference techniques leads to the best performance, under the assumption that the sensor is sufficiently stable.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Scene identification probabilities for evaluating radiation flux errors due to scene misidentification

The scene identification probabilities (Pij) are fundamentally important in evaluations of the top-of-the-atmosphere (TOA) radiation-flux errors due to the scene misidentification. In this paper, the scene identification error probabilities were empirically derived from data collected in 1985 by the Earth Radiation Budget Experiment (ERBE) scanning radiometer when the ERBE satellite and the NOAA-9 spacecraft were rotated so as to scan alongside during brief periods in January and August 1985. Radiation-flux error computations utilizing these probabilities were performed, using orbit specifications for the ERBE, the Cloud and Earth's Radiant Energy System (CERES), and the SCARAB missions for a scene that was identified as partly cloudy over ocean. Typical values of the standard deviation of the random shortwave error were in the order of 1.5-5 W/sq m, but could reach values as high as 18.0 W/sq m as computed from NOAA-9.

Manalo, Natividad D.↗

Spatial sampling errors for a satellite-borne scanning radiometer

The Clouds and Earth's Radiant Energy System (CERES) scanning radiometer is planned as the Earth radiation budget instrument for the Earth Observation System, to be flown in the late 1990's. In order to minimize the spatial sampling errors of the measurements, it is necessary to select design parameters for the instrument such that the resulting point spread function will minimize spatial sampling errors. These errors are described as aliasing and blurring errors. Aliasing errors are due to presence in the measurements of spatial frequencies beyond the Nyquist frequency, and blurring errors are due to attenuation of frequencies below the Nyquist frequency. The design parameters include pixel shape and dimensions, sampling rate, scan period, and time constants of the measurements. For a satellite-borne scanning radiometer, the pixel footprint grows quickly at large nadir angles. The aliasing errors thus decrease with increasing scan angle, but the blurring errors grow quickly. The best design minimizes the sum of these two errors over a range of scan angles. Results of a parameter study are presented, showing effects of data rates, pixel dimensions, spacecraft altitude, and distance from the spacecraft track.

Manalo, Natividad D.↗

Accuracy of earth albedo estimates from wide-angle radiation measurements

The instantaneous resolution and accuracy that can be expected from two inversion techniques to be used in the Earth Radiation Budget Experiment (ERBE) are examined. It is shown that measurement errors are magnified by the numerical filter and that this magnification is a function of the magnitude of the inversion factor. By using singular value decomposition, these magnitudes can be reduced to improve the estimates. A simulation of the estimation process using an albedo field derived from scanning radiometer data shows that retaining 6 of the 13 singular values gives the best results. If there are no bidirectional model errors the medium-field-of-view data give the best estimates. However, when bidirectional model errors are considered, the wide-field-of-view measurements give better estimates since they are less sensitive to these errors.

Green, R. N.↗

Comparison of cloud fields from atmospheric general circulation model, in situ and satellite measurements

This paper focuses on the comparison of cloud amounts derived from an atmospheric general circulation model (AGCM), Satellite-observed clouds, and Ground-based cloud observations. Unlike Earth Radiation Budget Experiment (ERBE)-type comparisons it does not mix potential errors in the cloud amount with those in the radiation code embedded in the model. Long term cloud climatologies were used to compare global cloud amounts and regional seasonal cycles. The AGCM successfully reproduced the signatures of the warm pool and North Pacific seasonal cycle cloudiness but failed in the low stratus region off the coast of South America, a known problem for AGCMs. The data sets also reproduced the anomaly signature associated with El Nino in the warm pool region, but the model amounts were lower. Global results had a similar success rate, with the model generally producing lower total cloud compared to the satellite and in situ measurements. To compare cloud vertical distributions the cloud height may need to be validated using the corresponding radiation fields. Unfortunately there were also some large discrepancies between the two observed cloud data sets. While tremendously improved over the last decade the character of the observed cloud data sets, must be substantially enhanced before they will be useful in validating AGCMs by any but the crudest levels of comparison.

Haskins, Robert D.↗

Comparison of TRMM 2A25 Products Version 6 and Version 7 with NOAA/NSSL Ground Radar-Based National Mosaic QPE

Characterization of the error associated to satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving spaceborne passive and active microwave measurements for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. We focus here on the error structure of Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) quantitative precipitation estimation (QPE) at ground. The problem was addressed in a previous paper by comparison of 2A25 version 6 (V6) product with reference values derived from NOAA/NSSL's ground radar-based National Mosaic and QPE system (NMQ/Q2). The primary contribution of this study is to compare the new 2A25 version 7 (V7) products that were recently released as a replacement of V6. This new version is considered superior over land areas. Several aspects of the two versions are compared and quantified including rainfall rate distributions, systematic biases, and random errors. All analyses indicate V7 is an improvement over V6.

Kirstetter, Pierre-Emmanuel↗

Toward a Framework for Systematic Error Modeling of NASA Spaceborne Radar with NOAA/NSSL Ground Radar-Based National Mosaic QPE

Characterization of the error associated to satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving space-born passive and active microwave measurement") for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. We focus here on the error structure of NASA's Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) quantitative precipitation estimation (QPE) at ground. The problem is addressed by comparison of PR QPEs with reference values derived from ground-based measurements using NOAA/NSSL ground radar-based National Mosaic and QPE system (NMQ/Q2). A preliminary investigation of this subject has been carried out at the PR estimation scale (instantaneous and 5 km) using a three-month data sample in the southern part of US. The primary contribution of this study is the presentation of the detailed steps required to derive trustworthy reference rainfall dataset from Q2 at the PR pixel resolution. It relics on a bias correction and a radar quality index, both of which provide a basis to filter out the less trustworthy Q2 values. Several aspects of PR errors arc revealed and quantified including sensitivity to the processing steps with the reference rainfall, comparisons of rainfall detectability and rainfall rate distributions, spatial representativeness of error, and separation of systematic biases and random errors. The methodology and framework developed herein applies more generally to rainfall rate estimates from other sensors onboard low-earth orbiting satellites such as microwave imagers and dual-wavelength radars such as with the Global Precipitation Measurement (GPM) mission.

Kirstettier, Pierre-Emmanual↗

The Plane-parallel Albedo Bias of Liquid Clouds from MODIS Observations

In our most advanced modeling tools for climate change prediction, namely General Circulation Models (GCMs), the schemes used to calculate the budget of solar and thermal radiation commonly assume that clouds are horizontally homogeneous at scales as large as a few hundred kilometers. However, this assumption, used for convenience, computational speed, and lack of knowledge on cloud small scale variability, leads to erroneous estimates of the radiation budget. This paper provides a global picture of the solar radiation errors at scales of approximately 100 km due to warm (liquid phase) clouds only. To achieve this, we use cloud retrievals from the instrument MODIS on the Terra and Aqua satellites, along with atmospheric and surface information, as input into a GCM-style radiative transfer algorithm. Since the MODIS product contains information on cloud variability below 100 km we can run the radiation algorithm both for the variable and the (assumed) homogeneous clouds. The difference between these calculations for reflected or transmitted solar radiation constitutes the bias that GCMs would commit if they were able to perfectly predict the properties of warm clouds, but then assumed they were homogeneous for radiation calculations. We find that the global average of this bias is approx.2-3 times larger in terms of energy than the additional amount of thermal energy that would be trapped if we were to double carbon dioxide from current concentrations. We should therefore make a greater effort to predict horizontal cloud variability in GCMs and account for its effects in radiation calculations.

Oreopoulos, Lazaros↗

The Climate Signal in Regional Moisture Fluxes: A Comparison of Three Global Data Assimilation Products

This study assesses the quality of estimates of climate variability in moisture flux and convergence from three assimilated data sets: two are reanalysis products generated at the Goddard Data Assimilation Office (DAO) and the National Centers for Environmental Prediction/National Centers for Atmospheric Research (NCEPJNCAR), and the third consists of the operational analyses generated at the European Center for Medium Range Forecasts (ECMWF). The regions under study (the United States Great Plains, the Indian monsoon region, and Argentina east of the Andes) are characterized by frequent low level jets (LLJs) and other interannual low level wind variations tied to the large-scale flow. While the emphasis is on the reanalysis products, the comparison with the operational product is provided to help assess the improvements gained from a fixed analysis system. All three analyses capture the main moisture flux anomalies associated with selected extreme climate (drought and flood) events during the period 1985-93. The correspondence is strongest over the Great Plains and weakest over the Indian monsoon region reflecting differences in the observational coverage. For the reanalysis products, the uncertainties in the lower tropospheric winds is by far the dominant source of the discrepancies in the moisture flux anomalies in the middle latitude regions. Only in the Indian Monsoon region, where interannual variability in the low level winds is comparatively small, does the moisture bias play a substantial role. In contrast, the comparisons with the operational product show differences in moisture which are comparable torhe differences in the wind in all three regions. Compared with the fluxes, the anomalous moisture convergences show substantially larger differences among the three products. The best agreement occurs over the Great Plains region where all three products show vertically-integrated moisture convergence during the floods and divergence during the drought with differences in magnitude of about 25%. The reanalysis products, in particular, show good agreement in depicting the different roles of the mean flow and transients during the flood and drought periods. Differences between the three products in the other two regions exceed 100% reflecting differences in the low level jets and the large scale circulation patterns. The operational product tends to have locally larger amplitude convergence fields which average out in area-mean budgets: this appears to be at least in part due to errors in the surface pressure fields and aliasing from the higher resolution of the original ECMWF fields. On average, the reanalysis products show higher coherence with each other than with the operational product in the estimates of interannual variability. This result is less clear in the Indian monsoon region where differences in the input observations appears to be an important factor. The agreement in the anomalous convergence patterns is, however, still rather poor even over relatively data dense regions such as the United States Great Plains. These differences are attributed to deficiencies in the assimilating GCM's representations of the planetary boundary layer and orography, and a global observing system incapable of resolving the highly confined low level winds associated with the climate anomalies.

Min, Wei↗

Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder Intercalibration Data Analysis Strategy

One of the prime science objectives of NASA’s Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) mission is to acquire unprecedentedly accurate Système Internationale (SI)-traceable Earth-view measurements that can be used as reference for intercalibrating the Clouds and the Earth’s Radiant Energy System (CERES) and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments onboard NOAA-20 satellite. The hyperspectral nature of CPF measurements will significantly reduce spectrally induced biases when intercalibrating multiband or broadband satellite instruments with CPF. This advancement eliminates the requirement for spectral band adjustment factors, representing a substantial improvement in sensor intercalibration studies. The CPF intercalibration team is aiming to achieve a maximum intercalibration methodology uncertainty of 0.3 % (k=1). Our studies have revealed that the most significant contribution to the targeted uncertainty budget originates from the combined effects of spatial and temporal matching errors. Spatial matching error arises from discrepancies in CPF and target instrument pixel resolution and geolocation uncertainty, while temporal matching error is caused by changes in scene radiances over time, occurring between when the target and reference instruments observe the same scenes. To estimate the maximum expected uncertainty contribution from these sources, spatial and temporal matching noise analyses were conducted using algorithmically filtered Landsat 9 Operational Land Imager (OLI) and Geostationary Operational Environmental Satellite (GOES)-16 ABI CONUS scan data as proxies for CPF and target instruments. In the upcoming conference presentation, we will elaborate on the methodology employed in these experiments, provide details of the data filtering algorithms, and present results of the spatial and temporal matching uncertainty analyses.

Intercalibration↗

Study of the Effect of Temporal Sampling Frequency on DSCOVR Observations Using the GEOS-5 Nature Run Results (Part I): Earths Radiation Budget

Satellites always sample the Earth-atmosphere system in a finite temporal resolution. This study investigates the effect of sampling frequency on the satellite-derived Earth radiation budget, with the Deep Space Climate Observatory (DSCOVR) as an example. The output from NASA's Goddard Earth Observing System Version 5 (GEOS-5) Nature Run is used as the truth. The Nature Run is a high spatial and temporal resolution atmospheric simulation spanning a two-year period. The effect of temporal resolution on potential DSCOVR observations is assessed by sampling the full Nature Run data with 1-h to 24-h frequencies. The uncertainty associated with a given sampling frequency is measured by computing means over daily, monthly, seasonal and annual intervals and determining the spread across different possible starting points. The skill with which a particular sampling frequency captures the structure of the full time series is measured using correlations and normalized errors. Results show that higher sampling frequency gives more information and less uncertainty in the derived radiation budget. A sampling frequency coarser than every 4 h results in significant error. Correlations between true and sampled time series also decrease more rapidly for a sampling frequency less than 4 h.

GEOS-5↗