Kevin West Bowman

Importance of rain evaporation and continental convection in the tropical water cycle.

Atmospheric moisture cycling is an important aspect of the Earth’s climate system, yet the processes determining atmospheric humidity are poorly understood. For example, direct evaporation of rain contributes significantly to the heat and moisture budgets of clouds, but few observations of these processes are available. Similarly, the relative contributions to atmospheric moisture over land from local evaporation and humidity from oceanic sources are uncertain. Lighter isotopes of water vapour preferentially evaporate whereas heavier isotopes preferentially condense and the isotopic composition of ocean water is known. Here we use this information combined with global measurements of the isotopic composition of tropospheric water vapour from the Tropospheric Emission Spectrometer (TES) aboard the Aura spacecraft, to investigate aspects of the atmospheric hydrological cycle that are not well constrained by observations of precipitation or atmospheric vapour content. Our measurements of the isotopic composition of water vapour near tropical clouds suggest that rainfall evaporation contributes significantly to lower troposphere humidity, with typically 20% and up to 50% of rainfall evaporating near convective clouds. Over the tropical continents the isotopic signature of tropospheric water vapour differs significantly from that of precipitation, suggesting that convection of vapour from both oceanic sources and evapotranspiration are the dominant moisture sources. Our measurements allow an assessment of the intensity of the present hydrological cycle and will help identify any future changes as they occur.

Intercomparison of near-real-time biomass burning emissions estimates constrained by satellite fire data

We compare biomass burning emissions estimates from four different techniques that use satellite based fire products to determine area burned over regional to global domains. Three of the techniques use active fire detections from polar-orbiting MODIS sensors and one uses detections and instantaneous fire size estimates from geostationary GOES sensors. Each technique uses a different approach for estimating trace gas and particulate emissions from active fires. Here we evaluate monthly area burned and CO emission estimates for most of 2006 over the contiguous United States domain common to all four techniques. Two techniques provide global estimates and these are also compared. Overall we find consistency in temporal evolution and spatial patterns but differences in these monthly estimates can be as large as a factor of 10. One set of emission estimates is evaluated by comparing model CO predictions with satellite observations over regions where biomass burning is significant. These emissions are consistent with observations over the US but have a high bias in three out of four regions of large tropical burning. The large-scale evaluations of the magnitudes and characteristics of the differences presented here are a necessary first step toward an ultimate goal of reducing the large uncertainties in biomass burning emission estimates, thereby enhancing environmental monitoring and prediction capabilities.

TES level 1 algorithms: interferogram processing, geolocation, radiometric, and spectral calibration

The Tropospheric Emission Spectrometer (TES) on the Earth Observing System (EOS) Aura satellite measures the infrared radiance emitted by the Earths surface and atmosphere using Fourier transform spectrometry. The measured interferograms are converted into geolocated, calibrated radiance spectra by the L1 (Level 1) processing, and are the inputs to L2 (Level 2) retrievals of atmospheric parameters, such as vertical profiles of trace gas abundance. We describe the algorithmic components of TES Level 1 processing, giving examples of the intermediate results and diagnostics that are necessary for creating TES L1 products. An assessment of noise-equivalent spectral radiance levels and current systematic errors is provided. As an initial validation of our spectral radiances, TES data are compared to the Atmospheric Infrared Sounder (AIRS) (on EOS Aqua), after accounting for spectral resolution differences by applying the AIRS spectral response function to the TES spectra. For the TES L1 nadir data products currently available, the agreement with AIRS is 1 K or better.

Information Theoretic Metrics to Characterize Observations in Variational Data Assimilation

Abstract Data assimilation obtains improved estimates of the state of a physical system by combining imperfect model results with sparse and noisy observations of reality. Not all observations used in data assimilation are equally valuable. The ability to characterize the usefulness of different observation locations is important for analyzing the effectiveness of the assimilation system, for data pruning, and for the design of future sensor systems. This paper proposes a new approach to characterizes the usefulness of different observation in four dimensional variational (4D-Var) data assimilation. Metrics from information theory are used to quantify the contribution of observations to decreasing uncertainty with which the system state is known. We derive ensemble based, computationally feasible procedures to estimate the information content of various observations.

Application of wavelets to wavefront reconstruction in adaptive optical systems

One of the principle difficulties with popular wavefront reconstruction techniques such as least-squares estimators is that they are computationally intensive. In this paper a least-squares reconstructor is represented in terms of a 4-D wavelet basis. This representation is called the multiresolution wavefront reconstructor (MWR). A thresholding operation is applied to the MWR in order to remove wavelet coefficients of negligible magnitude. The resulting thresholded reconstructor matrix is sparse, leading to an estimate calculated in O(N3) operations, as opposed to O(N4) operations for the standard least-squares wavefront reconstructor. The thresholded multiresolution wavefront reconstructor is compared with other techniques in terms of computational complexity.

Evaluation And Attribution Of OCO-2 XCO 2 Uncertainties

Evaluating and attributing uncertainties in total column atmospheric CO2 measurements (XCO2) from the OCO-2 instrument is critical for testing hypotheses related to the underlying processes controlling XCO2 and for developing quality flags needed to choose those measurements that are usable for carbon cycle science. Here we test the reported uncertainties of version 7 OCO2 XCO2 measurements by examining variations of the XCO2 measurements and their calculated uncertainties within small regions (∼ 100 km× 10.5 km) in which natural CO2 variability is expected to be small relative to variations imparted by noise or interferences. Over 39 000 of these “small neighborhoods” comprised of approximately 190 observations per neighborhood are used for this analysis. We find that a typical ocean measurement has a precision and accuracy of 0.35 and 0.24 ppm respectively for calculated precisions larger than ∼ 0.25 ppm. These values are approximately consistent with the calculated errors of 0.33 and 0.14 ppm for the noise and interference error, assuming that the accuracy is bounded by the calculated interference error. The actual precision for ocean data becomes worse as the signal-to-noise increases or the calculated precision decreases below 0.25 ppm for reasons that are not well understood. A typical land measurement, both nadir and glint, is found to have a precision and accuracy of approximately 0.75 and 0.65 ppm respectively as compared to the calculated precision and accuracy of approximately 0.36 and 0.2 ppm. The differences in accuracy between ocean and land suggests that the accuracy of XCO2 data is likely related to interferences such as aerosols or surface albedo as they vary less over ocean than land. The accuracy as derived here is also likely a lower bound as it does not account for possible systematic biases between the regions used in this analysis.

Comparison between the Local Ensemble Transform Kalman Filter (LETKF) and 4D‐Var in atmospheric CO2 flux inversion with the Goddard Earth Observing System‐Chem model and the observation impact diagnostics from the LETKF

Ensemble Kalman filter (EnKF) and 4D-Variational (4D-Var) are two advanced data assimilation methods that are the basis of numerical weather prediction and have been extensively used in trace gas assimilation and inverse modeling. In this study, we compare 4D-Var and the Local Ensemble Transform Kalman Filter (LETKF), one type of EnKF, in estimating CO2 fluxes with both simulated and real satellite data from Greenhouse gases Observing Satellite (GOSAT) and propose a method to calculate flux changes and flux error reductions from assimilating each observation within the LETKF. The results show that the mean posterior flux accuracy across 11 land regions defined by the Atmospheric Tracer Transport Model Intercomparison Project is comparable between 4D-Var and the LETKF, as shown in the Observing System Simulation Experiment, but the differences between the LETKF and 4D-Var are relatively larger over data sparse regions. We show that this is most likely due to the fact that the observations from a much broader region have impact on flux estimation in 4D-Var than in the LETKF. As a result, the posterior fluxes from 4D-Var are more consistent with the atmospheric CO2 growth rate. We find that the inversion results are less dependent on inversion methods with the increase of observations. With real GOSAT observations, we show that the posterior flux changes in 2011 relative to 2010 are more consistent between these two methods than the absolute estimates.

A method for independent validation of surface fluxes from atmospheric inversion: Application to CO2

Validating fluxes from an atmospheric inversion is a challenging problem because there are often no direct flux measurements at comparable spatiotemporal scales whereas there are often relevant independent observables, e.g., trace gas concentrations. In this paper, we propose a method that validates posterior fluxes by projecting the errors between posterior and prior observable model states and independent data to the spatiotemporal differences between posterior and prior fluxes with an atmospheric transport adjoint model. We prove theoretically the conditions for which observed error reductions lead to error reductions in fluxes. We apply this approach to the atmospheric CO2 inversion problem using the NASA Carbon Monitoring System Flux project with an Observing System Simulation Experiment. We show that the posterior fluxes are more accurate than the prior over the region that significantly contributes to the reduction of CO2 errors, which is consistent with the theory.

Sensor-web Operations Explorer (SOX) for Earth Science Air Quality Mission Concepts

Future air quality missions will face significant measurement strategy design and implementation challenges. Characterizing the atmospheric state and its impact on air quality requires observations of trace gases (e.g., ozone [O3], carbon monoxide [CO], nitrogen dioxide [NO2], sulfur dioxide [SO2]), aerosols (e.g., size and shape distributions, composition), clouds (e.g., type, height, sky coverage), and physical parameters (e.g., temperature, pressure, humidity) across temporal and spatial scales that range from minutes to days and from meters to > 10,000 km. Validating satellite measurements is another major challenge, and it requires well organized and orchestrated sub-orbital sensor web deployments. No single sensor, instrument, platform, or network can provide all of the information necessary to address this issue. Constellations of spacecraft, integrated air-borne campaigns, and distributed sensor networks have been actively pursued to achieve the needed multi-dimensional observation coverage. However, these complicated sensor webs must address how to formulate the complex design trade space, how to explore the trade space rapidly, how to establish evaluation metrics, and how to coordinate observations optimally. The Sensor-web Operations Explorer (SOX) research task under the NASA Earth Science Technology Office addresses these challenges by creating a virtual sensor-web experiment framework that can support orbital and sub-orbital observation system simulation experiment.

Improving GEOS-Chem Model Tropospheric Ozone through Assimilation of Pseudo Tropospheric Emission Spectrometer Profile Retrievals

4D-variational or adjoint-based data assimilation provides a powerful means for integrating observations with models to estimate an optimal atmospheric state and to characterize the sensitivity of that state to the processes controlling it.In this paper we present the improvement of 2006 summer time distribution of global tropospheric ozone through assimilation of pseudo profile retrievals from the Tropospheric Emission Spectrometer (TES) into the GEOS-Chem global chemical transport model based on a recently-developed adjoint model of GEOS-Chem v7. We are the first to construct an adjoint of the linearized ozone parameterization (linoz) scheme that can be of very high importance in quantifying the amount of tropospheric ozone due to upper boundary exchanges. Tests conducted at various geographical levels show that the mismatch between adjoint values and their finite difference approximations could be up to 87% if linoz module adjoint is not used, leading to a divergence in the quasi-Newton approximation algorithm (L-BFGS) during data assimilation. We also present performance improvements in this adjoint model in terms of memory usage and speed. With the parallelization of each science process adjoint subroutine and sub-optimal combination of checkpoints and recalculations, the improved adjoint model is as efficient as the forward GEOS-Chem model.