Chi O. Ao

Achieving Climate Change Absolute Accuracy in Orbit

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission will provide a calibration laboratory in orbit for the purpose of accurately measuring and attributing climate change. CLARREO measurements establish new climate change benchmarks with high absolute radiometric accuracy and high statistical confidence across a wide range of essential climate variables. CLARREOs inherently high absolute accuracy will be verified and traceable on orbit to Systeme Internationale (SI) units. The benchmarks established by CLARREO will be critical for assessing changes in the Earth system and climate model predictive capabilities for decades into the future as society works to meet the challenge of optimizing strategies for mitigating and adapting to climate change. The CLARREO benchmarks are derived from measurements of the Earths thermal infrared spectrum (5–50 μm), the spectrum of solar radiation reflected by the Earth and its atmosphere (320–2300 nm), and radio occultation refractivity from which...

Assessing the performance of GPS radio occultation measurements in retrieving tropospheric humidity in cloudiness: A comparison study with radiosondes, ERA-Interim, and AIRS data sets

We assess the impact that the Global Positioning System radio occultations (GPSRO) measurements have on complementing different data sets in characterizing the lower-to-middle tropospheric humidity in cloudy conditions over both land and oceans using data from 1 August 2006 to 31 October 2006. We use observations from rawinsondes, Global Positioning System radio occultations (GPSRO), Atmospheric Infrared Sounder (AIRS), and the European Center for Medium-Range Weather Forecasts Reanalysis Interim (ERA-Interim). During the selected time period, Constellation Observing System for Meteorology, Ionosphere, and Climate data were not assimilated in ERA-Interim. From each data set, we estimate a zonally averaged tropospheric specific humidity profile at tropical, middle, and high latitudes. Over land, we use rawinsondes as the ground truth and quantify the specific humidity differences and root-mean-square-errors (RMSEs) of the GPSRO, AIRS, and ERA-Interim profiles. GPSRO are beneficial in retrieving lower tropospheric humidity than upper tropospheric profiles, due to their loss of sensitivity at high altitudes. Blending GPSRO with ERA-Interim produces profiles with smaller humidity biases outside the tropics, but GPSRO data do not improve the humidity RMSE when compared to rawinsondes. Combining GPSRO with AIRS leads to smaller humidity bias at the tropics and high latitudes, while reducing humiditys RMSEs. Over oceans, no rawinsonde information is available, and we use ERA-Interim as a reference. Combining GPSRO with AIRS leads to smaller humidity RMSEs than AIRS. We conclude that cross-comparisons and synergies among multi-instrument observations are promising in advancing our knowledge of the tropospheric humidity in cloudy conditions. GPSRO data can contribute to improving humidity retrievals over cloud-covered regions, especially over land and within the boundary layer.

Applications of COSMIC Radio Occultation Data from the Troposphere to Ionosphere and Potential Impacts of COSMIC-2 Data

What: More than 130 people representing 15 nations met to highlight accomplishments in global positioning system (GPS) radio occultation (RO) operations and algorithm development, meteorology, climate, and ionospheric applications using COSMIC data. When: 30 October–1 November 2012 Where: Boulder, Colorado APPLICATIONS OF COSMIC RADIO OCCULTATION DATA FROM THE TROPOSPHERE TO IONOSPHERE AND POTENTIAL IMPACTS OF COSMIC-2 DATA

Backpropagation Processing of GPS Radio Occultation Data

We provide an assessment of the backpropagation (BP) method for processing GPS radio occultations using simulations as well as recent data from CHAMP and SAC-C. It is found that BP gives improved retrievals over the standard Doppler technique, even when multipath ambiguities are not completely removed. In addition, by being an amplitude-weighted algorithm, BP is robust in the presence of receiver errors that arise when signals with low SNR are tracked.

Estimation of Winds from GPS Radio Occultations

GPS radio occultations (RO) offer the possibility to map winds in the upper troposphere and lower stratosphere (UTLS) region because geopotential height is the independent coordinate of retrieval. Most other sounders do not offer this possibility because their independent coordinate of retrieval is pressure. To estimate theprecisionwithwhichGPSradiooccultationdatacanmapwinds,drypressureprofilesaresimulatedfromthe Interim European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-Interim) at the actual locations of the Challenging Minisatellite Payload (CHAMP) and the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) soundings for the year 2007. Monthly wind maps were created by using Bayesian interpolation on subsampled ERA-Interim data in 3‐5-day bins and subsequent averagingoveramonth.Mappingwindsinthisapproachrequiresthat1)geostrophyapproximateswinds;2)dry pressureapproximatespressureintheUTLS;and3)geopotentialheightcanbemappedaccuratelygivensparse, nonuniform distributions of data. This study found that, under these conditions, it is possible to map monthly windsnearthetropopausewithanaccuracyof5.6ms 21 withCHAMPaloneand4.5ms 21 withCOSMICalone. Thedominantcontributorstouncertaintyareundersamplingoftheatmosphereandageostrophy,particularlyat the leading and trailing edges of the subtropical jet. The former is reduced with increased density of GPS RO soundings. The latter cannot be reduced even after iteration for balanced winds. Nevertheless, it is still possible tocapturethegeneralwindpatternandtodeterminethepositionofthesubtropicaljetdespitetheuncertaintyin its magnitude. COSMIC radio occultation measurements from 2006 through 2011 were used to estimate monthly geostrophic winds maps in UTLS. The resultant wind dataset is posted online.

Mapping GPS Radio Occultation Data by Bayesian Interpolation

Bayesianinterpolationformapping GPS radio occultation data on a sphereis exploredandits performance evaluated. Bayesian interpolation is ideally suited to the task of fitting data randomly and nonuniformly distributed with unknown error without overfitting the data. The geopotential height at dry pressure 200 hPa is simulated as data with theoretical distributions of the Challenging Mini-Satellite Payload (CHAMP) and of the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC). The simulated CHAMP data are found to be best fit with a spherical harmonic basis of 14th degree; the COSMIC data with a spherical harmonic basis of 20th degree. The best regularizer mimics a splinefit, and relaxing the penalty for purely meridional structures or for the global mean yields little advantage. Climatologies are most accurately established by binning in ’2-day intervals to best resolve synoptic structures in space and time. Finally, Bayesian interpolation is shown to negate a source of systematic sampling error obtained in binning and averaging highly nonuniform data but to incur another systematic error due to incomplete resolution of the background atmosphere, notably in the Southern Hemisphere.

Evaluation of CMIP5 upper troposphere and lower stratosphere geopotential height with GPS radio occultation observations

We present a detailed comparison of geopotential height fields between the Coupled Model Inter-Comparison Project phase 5 (CMIP5) models and satellite observations from GPS radio occultation (RO). Our comparison focuses on the annual mean, seasonal cycle, and interannual variability of 200 hPa geopotential height in the years 2002–2008. Using a wide sample of atmosphere-only model runs (AMIP) from the CMIP5 archive, we find that most models agree well with the observations and weather reanalyses in the tropics in both the annual means and interannual variabilities. However, the agreement is poor over the extratropics with the largest model spreads in the high latitudes and the largest bias in the southern middle to high latitudes that persist all seasons. The models also show excessive seasonal variability over the Northern midlatitude land areas as well as the Southern Ocean but insufficient variability over the tropics and Antarctica. While the underlying causes for the model discrepancies require further analyses, this study demonstrates that global observations from GPS RO provide accurate benchmark-quality measurements in the upper troposphere and lower stratosphere through which biases in climate models as well as weather reanalyses can be identified.

Comparison of GPS/SAC-C and MIPAS/ENVISAT Temperature Profiles and Its Possible Implementation for EOS MLS Observations

This analysis presents comparisons of the atmospheric temperatures retrieved from GPS/SAC-C radio occultation observations using the JPL retrieval software, and from MIPAS/ENVISAT infrared spectrum measurements using the IMK data processor. Both individual profiles and zonal means of the atmospheric temperature at different seasons and geo-locations show reasonable agreement. For the temperatures at altitudes between 8–30 km, the mean differences between the correlative measurements are estimated at less than 2 K with rms deviations less than 5 K. A similar cross comparison technique can be used to help validate the observed temperatures from the new EOS MLS instrument, to be launched in 2004.

Using GPS radio occultations to infer the water vapor feedback

The air refractive index at L-band frequencies depends on the airs water vapor content and density. Exploiting this relationship, we derive for the first time a theoretical model to infer the specific humidity response to surface temperature variations, dq/dTs, given knowledge of how the air refractive index and temperature vary with surface temperature. We validate this model using 1.2–1.6 GHz Global Positioning System Radio Occultation (GPS RO) observations from 2007 to 2010 at 250 hPa, where the water vapor feedback on surface warming is strongest. The dq/dTs estimation from GPS RO observations shows excellent agreement with previously published results and the responses estimated using Atmospheric Infrared Sounder (AIRS) and NASAs Modern–Era Retrospective Analysis for Research and Applications (MERRA) data sets. Because of their high sensitivity to fractional changes in water vapor, current and future GPS RO observations show great promise in monitoring climate feedbacks and their trends.

Studying the Atmosphere Using Global Navigation Satellites

Observations acquired from space contribute significantly to scientific understanding of the Earths atmosphere. The atmosphere alters electromagnetic radiation, propagating through it in such a way that atmospheric properties can be gleaned from the interaction. Observations are broadly characterized into “active” and “passive” types, according to the source of the radiant energy.