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Showing papers by "Eugenia Kalnay published in 2020"


Journal ArticleDOI
TL;DR: The use of Observing System Simulation Experiments in the U.S. and their values and limitations are reviewed, leading to an expert consensus on five recommendations for further study.
Abstract: Capsule SummaryWe briefly review the use of Observing System Simulation Experiments in the U.S. and discuss their values and limitations, leading to an expert consensus on five recommendations for ...

31 citations


Journal ArticleDOI
TL;DR: This work argues that Carrying Capacity should not be prescribed but should instead be dynamically derived a posteriori from the bidirectional coupling of Earth System submodels with the Human System model, and demonstrates this approach with a minimal model of Human–Nature interaction.
Abstract: The Human System is within the Earth System. They should be modeled bidirectionally coupled, as they are in reality. The Human System is rapidly expanding, mostly due to consumption of fossil fuels...

5 citations


Journal ArticleDOI
13 Nov 2020
TL;DR: A perfect model experiment was performed using the HG in the SPEEDY model to show a new methodology to assign different weights to the two analyses, LETKF and 3D-Var in the generation of the final analysis, and the dynamically weighted HG analyses are significantly more balanced than the original HG analyses.
Abstract: Hybrid systems have become the state of the art among data assimilation methods. These systems combine the benefits of two other systems that are traditionally used in operational weather forecasti...

4 citations


Book ChapterDOI
01 Jan 2020
TL;DR: In this article, the Local Ensemble Transform Kalman Filter (LETKF) was implemented with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM), and JAXA's GSMaP (Global Satellite Mapping of Precipitation) data were assimilated at 112-km resolution.
Abstract: This chapter describes the authors’ effort on ensemble data assimilation of satellite precipitation measurements. The Local Ensemble Transform Kalman Filter (LETKF) was implemented with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM), and JAXA’s GSMaP (Global Satellite Mapping of Precipitation) data were assimilated at 112-km resolution.

2 citations


Journal ArticleDOI
TL;DR: The results show that the analyses and forecasts are improved the most by rejecting all the observations identified as detrimental by EFSO, but that major improvements also come from rejecting just the most detrimental 10% observations.
Abstract: Proactive quality control (PQC) is a fully flow dependent QC based on ensemble forecast sensitivity to observations (EFSO). Past studies showed in several independent cases that GFS forecasts can be improved by rejecting observations identified as detrimental by EFSO. However, the impact of cycling PQC in sequential data assimilation has, so far, only been examined using the simple Lorenz ’96 model. Using a low-resolution spectral GFS model that assimilates PrepBUFR (no radiance) observations with the local ensemble transform Kalman filter (LETKF), this study aims to become a bridge between a simple model and the implementation into complex operational systems. We demonstrate the major benefit of cycling PQC in a sequential data assimilation framework through the accumulation of improvements from previous PQC updates. Such accumulated PQC improvement is much larger than the “current” PQC improvement that would be obtained at each analysis cycle using “future” observations. As a result, it is unnecessary to use future information, and hence this allows the operational implementation of cycling PQC. The results show that the analyses and forecasts are improved the most by rejecting all the observations identified as detrimental by EFSO, but that major improvements also come from rejecting just the most detrimental 10% observations. The forecast improvements brought by PQC are observed throughout the 10 days of integration and provide more than a 12-h forecast lead-time gain. An important finding is that PQC not only reduces substantially the root-mean-squared forecast errors but also the forecast biases. We also show a case of “skill dropout,” where the control forecast misses a developing baroclinic instability, whereas the accumulated PQC corrections result in a good prediction.

2 citations