Luke Chen

AIRS: Improving Weather Forecasting and Providing New Data on Greenhouse Gases

Abstract The Atmospheric Infrared Sounder (AIRS) and its two companion microwave sounders, AMSU and HSB were launched into polar orbit onboard the NASA Aqua Satellite in May 2002. NASA required the sounding system to provide high-quality research data for climate studies and to meet NOAAs requirements for improving operational weather forecasting. The NOAA requirement translated into global retrieval of temperature and humidity profiles with accuracies approaching those of radiosondes. AIRS also provides new measurements of several greenhouse gases, such as CO2, CO, CH4, O3, SO2, and aerosols. The assimilation of AIRS data into operational weather forecasting has already demonstrated significant improvements in global forecast skill. At NOAA/NCEP, the improvement in the forecast skill achieved at 6 days is equivalent to gaining an extension of forecast capability of six hours. This improvement is quite significant when compared to other forecast improvements over the last decade. In addition to NCEP, ECM...

Satellite remote sounding of mid-tropospheric CO2

Human activity has increased the concentration of the earths atmospheric carbon dioxide, which plays a direct role in contributing to global warming. Mid-tropospheric CO_2 retrieved by the Atmospheric Infrared Sounder shows a substantial spatiotemporal variability that is supported by in situ aircraft measurements. The distribution of middle tropospheric CO_2 is strongly influenced by surface sources and large-scale circulations such as the mid-latitude jet streams and by synoptic weather systems, most notably in the summer hemisphere. In addition, the effects of stratosphere-troposphere exchange are observed during a final stratospheric warming event. The results provide the means to understand the sources and sinks and the lifting of CO_2 from surface layers into the free troposphere and its subsequent transport around the globe. These processes are not adequately represented in three chemistry-transport models that have been used to study carbon budgets.

Radiance Covariance and Climate Models

Spectral empirical orthogonal functions (EOFs) derived from the covariance of satellite radiance spectra may be interpreted in terms of the vertical distribution of the covariance of temperature, water vapor, and clouds. This has been done for four major geographic regions: the tropical oceans, midlatitude oceans, and three important land areas. The purpose of the investigation is to demonstrate the important constraints that resolved spectral radiances can place upon climate models.

A statistical method for testing a general circulation model with spectrally resolved satellite data

The motivation for this paper is to understand better the means available for testing climate models. Statistics of observed, outgoing, thermal spectra are compared with those predicted from a climate model, on the basis of data collected over a period of approximately 1 year. This is a powerful approach to testing a model with respect to processes internal to the atmosphere. These processes, which have characteristic timescales of less than a year, define the atmospheres response to external forcing. Second-moment statistics are particularly important for testing model variability, which is key to predicting the results of forcing the atmosphere, for example, by ocean surface temperature changes, increase of greenhouse gases, etc. Comparisons are presented between statistical data from the infrared interferometer spectrometer (IRIS), an orbiting fourier transform spectrometer, and spectra calculated using the medium-resolution spectral code, MODTRAN, applied to the temperature and humidity profiles from a well-known climate model. Ten months of IRIS data are available, and we have compared means, standard deviations, skew, and kurtosis of its spectrally resolved brightness temperature in three tropical regions for individual months and for a range of timescales. Also presented are comparisons of covariances using Empirical Orthogonal Functions (EOFs) calculated in frequency space. All data that are presented are based on radiance differences from two like spectra, which eliminates many of the errors generated by the use of MODTRAN and most of the errors due to calibration uncertainties in IRIS. Important differences (i.e., residuals) between the IRIS and the GCM statistics are found in comparisons, demonstrating that the spectral data can provide a severe test of many aspects of the variability of a general circulation model. We discuss some of the residuals and how they may be used to improve model performance in the context of an adjoint formalism. In the long run the only way to have confidence in the performance of a model is to subject it to as many discriminating comparisons with data as are practicable, and we present a good candidate.

Impact of increased water vapor on precipitation efficiency over northern Eurasia

This study investigates the relationships among water vapor, precipitation efficiency, precipitation amount, and air temperature anomalies on monthly time scales over northern Eurasia for winter and summer 2003–2010. Daily precipitation and temperature records at 505 historical stations, and atmospheric total precipitable water vapor and relative humidity data from Atmospheric Infrared Sounders, are used for analysis. Results show that higher atmospheric precipitable water associated with warmer temperature directly contributes to winter precipitation amount but has little impact on winter precipitation efficiency. However, accelerated decreasing relative humidity associated with higher temperature is the primary factor in the reduction of precipitation efficiency and precipitation amount regardless of higher precipitable water in summer. This study suggests that there are evident seasonal differences in precipitation trend associated with air temperature changes over the study region. Air temperature modifies a key atmospheric water variable that directly controls precipitation for that particular season.

The recycling rate of atmospheric moisture over the past two decades (1988?2009)

Numerical models predict that the recycling rate of atmospheric moisture decreases with time at the global scale, in response to global warming. A recent observational study (Wentz et al 2007 Science 317 233–5) did not agree with the results from numerical models. Here, we examine the recycling rate by using the latest data sets for precipitation and water vapor, and suggest a consistent view of the global recycling rate of atmospheric moisture between numerical models and observations. Our analyses show that the recycling rate of atmospheric moisture has also decreased over the global oceans during the past two decades. In addition, we find different temporal variations of the recycling rate in different regions when exploring the spatial pattern of the recycling rate. In particular, the recycling rate has increased in the high-precipitation region around the equator (i.e., the intertropical convergence zone) and decreased in the low-precipitation region located either side of the equator over the past two decades. Further exploration suggests that the temporal variation of precipitation is stronger than that of water vapor, which results in the positive trend of the recycling rate in the high-precipitation region and the negative trend of the recycling rate in the low-precipitation region.