scholarly journals Issues Regarding the Assimilation of Cloud and Precipitation Data

2007 ◽  
Vol 64 (11) ◽  
pp. 3785-3798 ◽  
Author(s):  
Ronald M. Errico ◽  
Peter Bauer ◽  
Jean-François Mahfouf
Keyword(s):  
Weather Prediction ◽  
Difficult Problem ◽  
Careful Examination ◽  
Weather Forecasts ◽  
Clear Sky ◽  
Precipitation Characteristics ◽  
Satellite Radiance Data ◽  
Prediction Systems ◽  
Radiance Data ◽  
Peculiar Nature

Abstract The assimilation of observations indicative of quantitative cloud and precipitation characteristics is desirable for improving weather forecasts. For many fundamental reasons, it is a more difficult problem than the assimilation of conventional or clear-sky satellite radiance data. These reasons include concerns regarding nonlinearity of the required observation operators (forward models), nonnormality and large variances of representativeness, retrieval, or observation–operator errors, validation using new measures, dynamic and thermodynamic balances, and possibly limited predictability. Some operational weather prediction systems already assimilate precipitation observations, but much more research and development remains. The apparently critical, fundamental, and peculiar nature of many issues regarding cloud and precipitation assimilation implies that their more careful examination will be required for accelerating progress.

scholarly journals Assessment of Radio Occultation Observations from the COSMIC-2 Mission with a Simplified Observing System Simulation Experiment Configuration

2017 ◽  
Vol 145 (9) ◽  
pp. 3581-3597 ◽  
Author(s):  
L. Cucurull ◽  
R. Li ◽  
T. R. Peevey
Keyword(s):  
Radio Occultation ◽  
Weather Forecasting ◽  
System Simulation ◽  
Weather Prediction ◽  
Forecast Model ◽  
Forecast Skill ◽  
Weather Forecasts ◽  
Global Coverage ◽  
Prediction Systems ◽  
Observing System Simulation Experiment

The mainstay of the global radio occultation (RO) system, the COSMIC constellation of six satellites launched in April 2006, is already past the end of its nominal lifetime and the number of soundings is rapidly declining because the constellation is degrading. For about the last decade, COSMIC profiles have been collected and their retrievals assimilated in numerical weather prediction systems to improve operational weather forecasts. The success of RO in increasing forecast skill and COSMIC’s aging constellation have motivated planning for the COSMIC-2 mission, a 12-satellite constellation to be deployed in two launches. The first six satellites (COSMIC-2A) are expected to be deployed in December 2017 in a low-inclination orbit for dense equatorial coverage, while the second six (COSMIC-2B) are expected to be launched later in a high-inclination orbit for global coverage. To evaluate the potential benefits from COSMIC-2, an earlier version of the NCEP’s operational forecast model and data assimilation system is used to conduct a series of observing system simulation experiments with simulated soundings from the COSMIC-2 mission. In agreement with earlier studies using real RO observations, the benefits from assimilating COSMIC-2 observations are found to be most significant in the Southern Hemisphere. No or very little gain in forecast skill is found by adding COSMIC-2A to COSMIC-2B, making the launch of COSMIC-2B more important for terrestrial global weather forecasting than that of COSMIC-2A. Furthermore, results suggest that further improvement in forecast skill might better be obtained with the addition of more RO observations with global coverage and other types of observations.

scholarly journals A Similarity-Based Implementation of the Schaake Shuffle

2016 ◽  
Vol 144 (5) ◽  
pp. 1909-1921 ◽  
Author(s):  
Roman Schefzik
Keyword(s):  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Similarity Criterion ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Prediction Horizon ◽  
Prediction Systems ◽  
Specific Implementation

Contemporary weather forecasts are typically based on ensemble prediction systems, which consist of multiple runs of numerical weather prediction models that vary with respect to the initial conditions and/or the parameterization of the atmosphere. Ensemble forecasts are frequently biased and show dispersion errors and thus need to be statistically postprocessed. However, current postprocessing approaches are often univariate and apply to a single weather quantity at a single location and for a single prediction horizon only, thereby failing to account for potentially crucial dependence structures. Nonparametric multivariate postprocessing methods based on empirical copulas, such as ensemble copula coupling or the Schaake shuffle, can address this shortcoming. A specific implementation of the Schaake shuffle, called the SimSchaake approach, is introduced. The SimSchaake method aggregates univariately postprocessed ensemble forecasts using dependence patterns from past observations. Specifically, the observations are taken from historical dates at which the ensemble forecasts resembled the current ensemble prediction with respect to a specific similarity criterion. The SimSchaake ensemble outperforms all reference ensembles in an application to ensemble forecasts for 2-m temperature from the European Centre for Medium-Range Weather Forecasts.

scholarly journals Accuracy of Balloon Trajectory Forecasts in the Lower Stratosphere

Atmosphere ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 102 ◽  
Author(s):  
Selvaraj Dharmalingam ◽  
Riwal Plougonven ◽  
Albert Hertzog ◽  
Aurélien Podglajen ◽  
Michael Rennie ◽  
...  
Keyword(s):  
Lower Stratosphere ◽  
Weather Prediction ◽  
Large Error ◽  
Vertical Resolution ◽  
Operational Strategies ◽  
Weather Forecasts ◽  
Long Duration ◽  
Median Error ◽  
Prediction Systems ◽  
The Impact

This paper investigates the accuracy of simulated long-duration super-pressure balloon trajectories in the lower stratosphere. The observed trajectories were made during the (tropical) Pre-Concordiasi and (polar) Concordiasi campaigns in 2010, while the simulated trajectories are computed using analyses and forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System model. In contrast with the polar stratosphere situation, modelling accurate winds in the tropical lower stratosphere remains challenging for numerical weather prediction systems. The accuracy of the simulated tropical trajectories are quantified with the operational products of 2010 and 2016 in order to understand the impact of model physics and vertical resolution improvements. The median errors in these trajectories are large (typically ≳ 250 km after 24 h), with a significant negative bias in longitude, for both model versions. In contrast, using analyses in which the balloon-borne winds have been assimilated reduces the median error in the balloon position after 24 h to ∼60 km. For future campaigns, we describe operational strategies that take advantage of the geographic distribution and the episodic nature of large error events to anticipate the amplitude of error in trajectory forecasts. We finally stress the importance of a high vertical resolution in the model, given the intense shears encountered in the tropical lower stratosphere.

scholarly journals Processing and quality control with FY-3C/GNOS data used in numerical weather prediction applications

2018 ◽  
Author(s):  
Mi Liao ◽  
Sean Healy ◽  
Peng Zhang
Keyword(s):  
Quality Control ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Noise Estimate ◽  
Ionosphere Model ◽  
Angle Space ◽  
Prediction Systems ◽  
L1 And L2

Abstract. The Chinese radio occultation sounder GNOS (Global Navigation Occultation Sounder) is on the FY-3C satellite, which was launched on September 23, 2013. Currently, GNOS data is transmitted via the Global Telecommunications System (GTS) providing 450–500 profiles per day for numerical weather prediction applications. This paper describes the processing for the GNOS profiles with large biases, related to L2 signal degradation. A new extrapolation procedure in bending angle space corrects the L2 bending angles, using a thin ionosphere model, and the fitting relationship between L1 and L2. We apply the approach to improve the L2 extrapolation of GNOS. The new method can effectively eliminate about 90 % of the large departures. In addition to the procedure for the L2 degradation, this paper also describes our quality control (QC) for FY-3C/GNOS. A noise estimate for the new L2 extrapolation can be used as a QC parameter to evaluate the performance of the extrapolation. Mean phase delays of L1 and L2 in the tangent height interval of 60 to 80 km are analysed and applied in the QC as well. A statistical comparison between GNOS and ECMWF (European Centre for Medium-Range Weather Forecasts) forecast data demonstrates that GNOS performs almost as well as GRAS, especially in the core region from around 10 to 35 km. The GNOS data with the new L2 extrapolation is suitable for assimilation into numerical weather prediction systems.

scholarly journals Introducing hydrometeor orientation into all-sky microwave/sub-millimeter assimilation

2020 ◽  
Author(s):  
Vasileios Barlakas ◽  
Alan J. Geer ◽  
Patrick Eriksson
Keyword(s):  
Weather Prediction ◽  
Weather Forecasts ◽  
Error Distributions ◽  
Order Of Magnitude ◽  
Microwave Imager ◽  
Prediction Systems ◽  
H Channels ◽  
Global Precipitation Mission ◽  
Microwave Radiances ◽  
Global Precipitation

Abstract. Numerical weather prediction systems still employ many simplifications when assimilating microwave radiances in all-sky conditions (clear sky, cloudy, and precipitation). For example, the orientation of ice hydrometeors is ignored, along with the polarization that this causes. We present a simple approach for approximating hydrometeor orientation, requiring minor adaption of software and no additional calculation burden. The approach is introduced in the RTTOV (Radiative Transfer for TOVS) forward operator and tested in the Integrated Forecast System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). For the first time within a data assimilation (DA) context, this represents the ice-induced brightness temperature differences between vertical (V) and horizontal (H) polarization, the polarization difference (PD). The discrepancies in PD between observations and simulations decrease by an order of magnitude at 166.5 GHz, with maximum reductions of 10–15 K. The error distributions, which were previously highly skewed and therefore problematic for DA, are now roughly symmetrical. The approach is based on rescaling the extinction in V- and H-channels, which is quantified by the polarization ratio ρ. Using dual polarization observations from Global Precipitation Mission microwave imager (GMI), suitable value for ρ was found to be 1.5 and 1.4 at 89.0 and 166.5 GHz, respectively. The scheme was used for all the conical scanners assimilated at ECMWF, with broadly neutral impact on the forecast, but with an increased physical consistency between instruments that employ different polarizations. This opens the way towards representing hydrometeor orientation for cross-track sounders, and at frequencies above 183.0 GHz where the polarization can be even stronger.

Improving numerical simulation of typhoon LEKIMA(2019) through assimilating ATMS radiance data

2020 ◽  
Author(s):  
Lei Zhang ◽  
Baode Chen
Keyword(s):  
Numerical Simulation ◽  
Data Assimilation ◽  
Inner Core ◽  
Weather Prediction ◽  
Advanced Technology ◽  
Limited Area ◽  
Numerical Weather ◽  
Satellite Radiance Data ◽  
Radiance Data ◽  
The Impact

<p>Lacking of high-resolution observations over oceans is one of the major problems for the numerical simulation of the tropical cyclones (TC), especially for the tropical cyclone inner-core structure’s simulation. Satellite observations plays an important role in improving the forecast skills of numerical weather prediction (NWP) systems. Many studies have suggested that the assimilation of satellite radiance data can substantially improve the numerical weather forecast skills for global model. However, the performance of satellite radiance data assimilation in limited-area modeling systems is still controversial.</p><p>This study attempts to investigate the impact of assimilation of the Advanced Technology Microwave Sounder (ATMS) satellite radiances data and its role to improve the model initial condition and forecast of typhoon LEKIMA(2019) using a regional mesoscale model. In this study, detailed analysis of the data impact will be presented, also the results from different data assimilation methods and different data usage schemes will be discussed.</p>

scholarly journals Introducing hydrometeor orientation into all-sky microwave and submillimeter assimilation

2021 ◽  
Vol 14 (5) ◽  
pp. 3427-3447
Author(s):  
Vasileios Barlakas ◽  
Alan J. Geer ◽  
Patrick Eriksson
Keyword(s):  
Weather Prediction ◽  
Weather Forecasts ◽  
Error Distributions ◽  
Order Of Magnitude ◽  
Microwave Imager ◽  
Prediction Systems ◽  
H Channels ◽  
Global Precipitation Mission ◽  
Microwave Radiances ◽  
Global Precipitation

Abstract. Numerical weather prediction systems still employ many simplifications when assimilating microwave radiances under all-sky conditions (clear sky, cloudy, and precipitation). For example, the orientation of ice hydrometeors is ignored, along with the polarization that this causes. We present a simple approach for approximating hydrometeor orientation, requiring minor adaption of software and no additional calculation burden. The approach is introduced in the RTTOV (Radiative Transfer for TOVS) forward operator and tested in the Integrated Forecast System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). For the first time within a data assimilation (DA) context, this represents the ice-induced brightness temperature differences between vertical (V) and horizontal (H) polarization – the polarization difference (PD). The discrepancies in PD between observations and simulations decrease by an order of magnitude at 166.5 GHz, with maximum reductions of 10–15 K. The error distributions, which were previously highly skewed and therefore problematic for DA, are now roughly symmetrical. The approach is based on rescaling the extinction in V and H channels, which is quantified by the polarization ratio ρ. Using dual-polarization observations from the Global Precipitation Mission microwave imager (GMI), suitable values for ρ were found to be 1.5 and 1.4 at 89.0 and 166.5 GHz, respectively. The scheme was used for all the conical scanners assimilated at ECMWF, with a broadly neutral impact on the forecast but with an increased physical consistency between instruments that employ different polarizations. This opens the way towards representing hydrometeor orientation for cross-track sounders and at frequencies above 183.0 GHz where the polarization can be even stronger.

Forecasting of Tornadoes and Downbursts

2020 ◽  
Author(s):  
Djordje Romanic
Keyword(s):  
Chaos Theory ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
The Past ◽  
Extreme Wind ◽  
Extreme Wind Speeds ◽  
Numerical Weather Prediction Models

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

Exploring trends for the H SAF ASCAT root-zone soil moisture data records

2021 ◽  
Author(s):  
David Fairbairn ◽  
Patricia de Rosnay ◽  
Peter Weston
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Weather Prediction ◽  
Climate Change Impacts ◽  
Soil Moisture Data ◽  
Negative Root ◽  
In Situ Observations ◽  
Prediction Systems ◽  
Water Scarce

<p>Environmental (e.g. floods, droughts) and weather prediction systems rely on an accurate representation of soil moisture (SM). The EUMETSAT H SAF aims to provide high quality satellite-based hydrological products, including SM.<br>ECMWF is producing ASCAT root zone SM for H SAF. The production relies on an Extended Kalman filter to retrieve root zone SM from surface SM satellite data. A 10 km sampling reanalysis product (1992-2020) forced by ERA5 atmospheric fields (H141/H142) is produced for H SAF, which assimilates ERS/SCAT (1992-2006) and ASCAT-A/B/C (2007-2020) derived surface SM. The root-zone SM performance is validated using sparse in situ observations globally and generally demonstrates a positive and consistent correlation over the period. A negative trend in root-zone SM is found during summer and autumn months over much of Europe during the period (1992-2020). This is consistent with expected climate change impacts and is particularly alarming over the water-scarce Mediterranean region. The recent hot and dry summer of 2019 and dry spring of 2020 are well captured by negative root-zone SM anomalies. Plans for the future H SAF data record products will be presented, including the assimilation of high-resolution EPS-SCA-derived soil moisture data.</p>

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