Experimental 1D + 4D‐Var assimilation of CloudSat observations

Janisková, Marta; Lopez, Philippe; Bauer, Péter

doi:10.1002/qj.988

Cited by 21 publications

(24 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, the variational approach is applied to a one-dimensional space plus time, which may suggest that the term "1 + 1D-VAR" may be more appropriate. Within the geophysical community, however, the term 1-D in 1D-VAR specifically refers to the vertical column, and is quite popular for radio occultation data (e.g., Eyre et al, 1993;Poli et al, 2002), total column water vapor (e.g., Marécal and Mahfouf, 2002;Bauer et al, 2006), cloud (e.g., Janiskova et al, 2012) assimilation etc. Since in the current study 1-D refers to a single dimension along the horizontal space and not necessarily the vertical column, we persist with usage of the term 4D-VAR rather than the term 1D-VAR.…”

Section: Estimation Methodsmentioning

confidence: 99%

Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO<sub>2</sub> data assimilation

Chatterjee

Michalak

2013

Atmos. Chem. Phys.

View full text Add to dashboard Cite

Abstract. Data assimilation (DA) approaches, including variational and the ensemble Kalman filter methods, provide a computationally efficient framework for solving the CO 2 source-sink estimation problem. Unlike DA applications for weather prediction and constituent assimilation, however, the advantages and disadvantages of DA approaches for CO 2 flux estimation have not been extensively explored. In this study, we compare and assess estimates from two advanced DA approaches (an ensemble square root filter and a variational technique) using a batch inverse modeling setup as a benchmark, within the context of a simple one-dimensional advection-diffusion prototypical inverse problem that has been designed to capture the nuances of a real CO 2 flux estimation problem. Experiments are designed to identify the impact of the observational density, heterogeneity, and uncertainty, as well as operational constraints (i.e., ensemble size, number of descent iterations) on the DA estimates relative to the estimates from a batch inverse modeling scheme. No dynamical model is explicitly specified for the DA approaches to keep the problem setup analogous to a typical real CO 2 flux estimation problem. Results demonstrate that the performance of the DA approaches depends on a complex interplay between the measurement network and the operational constraints. Overall, the variational approach (contingent on the availability of an adjoint transport model) more reliably captures the large-scale source-sink patterns. Conversely, the ensemble square root filter provides more realistic uncertainty estimates. Selection of one approach over the other must therefore be guided by the carbon science questions being asked and the operational constraints under which the approaches are being applied.

show abstract

Section: Estimation Methodsmentioning

confidence: 99%

Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO<sub>2</sub> data assimilation

Chatterjee

Michalak

2013

Atmos. Chem. Phys.

View full text Add to dashboard Cite

show abstract

“…(Buehler et al , )). In terms of active observations, the 94 GHz CloudSat radar has been used to evaluate ice clouds and snow in NWP models (Delanoë et al , ) and has been assimilated into models (Janisková et al , ). Such measurements will be continued with the Dopplerized 94 GHz radar on the forthcoming EarthCARE satellite (Illingworth et al , ).…”

Section: Introductionmentioning

confidence: 99%

Calculating the millimetre‐wave scattering phase function of snowflakes using the self‐similar Rayleigh–Gans Approximation

Hogan

Honeyager

Tyynelä

et al. 2017

Quart J Royal Meteoro Soc

103

View full text Add to dashboard Cite

Exploitation of millimetre-wave radiometer and radar observations of ice clouds and snow requires the ability to model the scattering properties of snowflakes. This article extends the Self-Similar Rayleigh-Gans Approximation (SSRGA) for rapid computation of the backscatter cross-section of ice aggregates, to compute the full scattering phase function, the scattering and absorption cross-sections and the asymmetry factor. We also show that the Rayleigh-Gans Approximation (RGA) may be improved to represent the enhanced scattering and absorption when the monomers from which the aggregate is composed are non-spherical. The new model is shown to perform well when compared to benchmark 94 and 183 GHz Discrete Dipole Approximation (DDA) calculations of the scattering by simulated unrimed aggregates of maximum dimension up to 1 cm that have a mass-size relationship in the range observed from aircraft. For denser particles, such as would result from riming, the validity of the underlying RGA becomes questionable and both the backscatter and scattering cross-sections can be underestimated by a factor of 2.

show abstract

“…In related areas, ECMWF is planning to assimilate reflectivities from cloud radar and lidar from the future EarthCARE mission directly, developing beyond the experimental 1D+4D‐Var framework of Janisková et al (). The assimilation of rain‐gauge observations has been demonstrated experimentally (Lopez, ), but is not planned for operational assimilation.…”

Section: Future Developments By Operational Centresmentioning

confidence: 99%

All‐sky satellite data assimilation at operational weather forecasting centres

Geer

Lonitz

Weston

et al. 2018

Quart J Royal Meteoro Soc

190

179

View full text Add to dashboard Cite

This article reviews developments towards assimilating cloud‐ and precipitation‐ affected satellite radiances at operational forecasting centres. Satellite data assimilation is moving beyond the “clear‐sky” approach that discards any observations affected by cloud. Some centres already assimilate cloud‐ and precipitation‐affected radiances operationally and the most popular approach is known as “all‐sky,” which assimilates all observations directly as radiances, whether they are clear, cloudy or precipitating, using models (for both radiative transfer and forecasting) that are capable of simulating cloud and precipitation with sufficient accuracy. Other frameworks are being tried, including the assimilation of humidity retrieved from cloudy observations using Bayesian techniques. Although the all‐sky technique is now proven for assimilation of microwave radiances, it has yet to be demonstrated operationally for infrared radiances, though several centres are getting close. Assimilating frequently available all‐sky infrared observations from geostationary satellites could give particular benefit for short‐range forecasting. More generally, assimilating cloud‐ and precipitation‐affected satellite observations improves forecasts in the medium range globally and can also improve the analysis and shorter‐range forecasting of otherwise poorly observed weather phenomena as diverse as tropical cyclones and wintertime low cloud.

show abstract

Experimental 1D + 4D‐Var assimilation of CloudSat observations

Cited by 21 publications

References 39 publications

Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO<sub>2</sub> data assimilation

Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO<sub>2</sub> data assimilation

Calculating the millimetre‐wave scattering phase function of snowflakes using the self‐similar Rayleigh–Gans Approximation

All‐sky satellite data assimilation at operational weather forecasting centres

Contact Info

Product

Resources

About

Experimental 1D + 4D‐Var assimilation of CloudSat observations

Cited by 21 publications

References 39 publications

Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO&lt;sub&gt;2&lt;/sub&gt; data assimilation

Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO&lt;sub&gt;2&lt;/sub&gt; data assimilation

Calculating the millimetre‐wave scattering phase function of snowflakes using the self‐similar Rayleigh–Gans Approximation

All‐sky satellite data assimilation at operational weather forecasting centres

Contact Info

Product

Resources

About

Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO<sub>2</sub> data assimilation

Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO<sub>2</sub> data assimilation