Twenty Years of Polar Winds from AVHRR: Validation and Comparison with ERA-40

Abstract Recent studies have shown that the Arctic climate has changed markedly over the past 20 years. Two major reanalysis products that can be used for studying recent changes unfortunately exhibit relatively large errors in the wind field over the Arctic where there are few radiosonde data available for assimilation. At least 10 numerical weather prediction centers worldwide have demonstrated that satellite-derived polar winds have a positive impact on global weather forecasts. The impact on reanalyses should be similar. Therefore, a polar wind dataset spanning more than 20 years was generated using Advanced Very High Resolution Radiometer (AVHRR) data. Comparisons with winds from radiosondes show biases in the AVHRR-derived winds of 0.1–0.8 m s−1, depending on the level. In addition, AVHRR has lower root-mean-square speed errors and speed biases than the 40-yr ECMWF reanalysis product (ERA-40) when compared with rawinsondes not assimilated into the reanalysis. Therefore, it is recommended that the historical AVHRR polar winds be assimilated into future versions of the reanalysis products. The authors also explore possible kinematic reasons for the disparities between ERA-40 and AVHRR wind fields. AVHRR and ERA-40 speed and direction differences for various kinematic flow features are investigated. Results show that, on average, AVHRR winds are faster in jet streams and ridges but are slower in troughs and jet exit regions. The results from this study could lead to a better dynamical understanding of why the reanalysis product produces a less-accurate wind vector field over regions that are void of radiosonde data.

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Improved impacts in observing system simulation experiments of radio occultation observations as a result of model and data assimilation changes

Monthly Weather Review ◽

10.1175/mwr-d-20-0174.1 ◽

2020 ◽

Author(s):

L. CUCURULL ◽

S. P. F. CASEY

Keyword(s):

Data Assimilation ◽

Radio Occultation ◽

Weather Forecasting ◽

Positive Impact ◽

System Simulation ◽

Weather Prediction ◽

Wind Fields ◽

Simulation Experiments ◽

The Impact ◽

Forward Operator

AbstractAs global data assimilation systems continue to evolve, Observing System Simulation Experiments (OSSEs) need to be updated to accurately quantify the impact of proposed observing technologies in weather forecasting. Earlier OSSEs with radio occultation (RO) observations have been updated and the impact of the originally proposed Constellation Observing Satellites for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) mission, with a high-inclination and low-inclination component, has been investigated by using the operational data assimilation system at NOAA and a 1-dimensional bending angle RO forward operator. It is found that the impact of the low-inclination component of the originally planned COSMIC-2 mission (now officially named COSMIC-2) has significantly increased as compared to earlier studies, and significant positive impact is now found globally in terms of mass and wind fields. These are encouraging results as COSMIC-2 was successfully launched in June 2019 and data have been recently released to operational weather centers. Earlier findings remain valid indicating that globally distributed RO observations are more important to improve weather prediction globally than a denser sampling of the tropical latitudes. Overall, the benefits reported here from assimilating RO soundings are much more significant than the impacts found in previous OSSEs. This is largely attributed to changes in the data assimilation and forecast system and less to the more advanced 1-dimensional forward operator chosen for the assimilation of RO observations.

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The Impacts of AMMA Radiosonde Data on the French Global Assimilation and Forecast System

Weather and Forecasting ◽

10.1175/2009waf2222237.1 ◽

2009 ◽

Vol 24 (5) ◽

pp. 1268-1286 ◽

Cited By ~ 23

Author(s):

C. Faccani ◽

F. Rabier ◽

N. Fourrié ◽

A. Agusti-Panareda ◽

F. Karbou ◽

...

Keyword(s):

Bias Correction ◽

Positive Impact ◽

Weather Prediction ◽

Radiosonde Data ◽

Weather Forecasts ◽

Western Africa ◽

Multidisciplinary Analysis ◽

Precipitation Estimates ◽

Vertical Density ◽

Surface Observations

Abstract The high vertical density soundings recorded during the 2006 African Monsoon Multidisciplinary Analysis (AMMA) campaign are assimilated into the French numerical weather prediction Action de Recherche Petite Echelle Grande Echelle (ARPEGE) four-dimensional variational data assimilation (4DVAR) system, with and without a bias correction for relative humidity. Four different experiments are carried out to assess the impacts of the added observations. The analyses and forecasts from these different scenarios are evaluated over western Africa. For the full experiment using all data together with a bias correction, the humidity analysis is in better agreement with surface observations and independent GPS observations than it was for the other experiments. AMMA data also improve the African easterly jet (AEJ) on its southeasterly side, and when they are used with an appropriate bias correction, the daily and monthly averaged precipitation results are in relatively good agreement with the satellite-based precipitation estimates. Forecast scores are computed with respect to surface observations, radiosondes, and analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF). The positive impacts of additional radiosonde observations (with a relevant bias correction) are found to propagate downstream with a positive impact over Europe at the 2–3-day forecast range.

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The Impact of Satellite-Derived Polar Winds on Lower-Latitude Forecasts

Monthly Weather Review ◽

10.1175/2009mwr2862.1 ◽

2010 ◽

Vol 138 (1) ◽

pp. 123-139 ◽

Cited By ~ 10

Author(s):

David Santek

Keyword(s):

Mass Balance ◽

Positive Impact ◽

Lower Latitude ◽

Wind Fields ◽

Jet Streams ◽

Substantial Impact ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Atmospheric Motion Vectors ◽

The Impact

Abstract The use of atmospheric motion vectors (AMVs) in NWP models continues to be an important source of information in data-sparse regions. These AMVs are derived from a time sequence of images from geostationary and polar-orbiting satellites. NWP centers have documented positive impact on model forecasts not only in regions where the AMVs are measured, but elsewhere as well. One example is the effect of the Moderate Resolution Imaging Spectroradiometer (MODIS) polar winds on forecasts in the middle and lower latitudes. Using a preoperational version of NCEP’s Global Forecast System (GFS), an experiment was run during August and September 2004, with and without the MODIS polar winds. Several cases within this period were analyzed to determine how winds poleward of 70° latitude affect the height and wind fields into lower latitudes. From the five cases examined, it was determined that the addition of the polar winds modifies the mass balance in synoptic-scale waves near the polar jet streams. This change in mass balance is evident in differences in the ageostrophic wind, which has an effect on the speed and amplitude of baroclinic waves that extends from the jet stream into lower latitudes in later forecast times. These results reveal the substantial impact that polar-only observations may have on the predictability of global weather systems.

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Impacts of Assimilating ATMS Radiances on Heavy Rainfall Forecast in RMAPS-ST

Remote Sensing ◽

10.3390/rs12071147 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1147

Author(s):

Yanhui Xie ◽

Min Chen ◽

Jiancheng Shi ◽

Shuiyong Fan ◽

Jing He ◽

...

Keyword(s):

Heavy Rainfall ◽

Initial Conditions ◽

Positive Impact ◽

Weather Prediction ◽

Advanced Technology ◽

Added Value ◽

Rainfall Forecast ◽

Hourly Rainfall ◽

Radiance Data ◽

The Impact

The Advanced Technology Microwave Sounder (ATMS) mounted on the Suomi National Polar-Orbiting Partnership (NPP) satellite can provide both temperature and humidity information for a weather prediction model. Based on the rapid-refresh multi-scale analysis and prediction system—short-term (RMAPS-ST), we investigated the impact of ATMS radiance data assimilation on strong rainfall forecasts. Two groups of experiments were conducted to forecast heavy precipitation over North China between 18 July and 20 July 2016. The initial conditions and forecast results from the two groups of experiments have been compared and evaluated against observations. In comparison with the first group of experiments that only assimilated conventional observations, some added value can be obtained for the initial conditions of temperature, humidity, and wind fields after assimilating ATMS radiance observations in the system. For the forecast results with the assimilation of ATMS radiances, the score skills of quantitative forecast rainfall have been improved when verified against the observed rainfall. The Heidke skill score (HSS) skills of 6-h accumulated precipitation in the 24-h forecasts were overall increased, more prominently so for the heavy rainfall above 25 mm in the 0–6 h of forecasts. Assimilating ATMS radiance data reduced the false alarm ratio of quantitative precipitation forecasting in the 0–12 h of the forecast range and thus improved the threat scores for the heavy rainfall storm. Furthermore, the assimilation of ATMS radiances improved the spatial distribution of hourly rainfall forecast with observations compared with that of the first group of experiments, and the mean absolute error was reduced in the 10-h lead time of forecasts. The inclusion of ATMS radiances provided more information for the vertical structure of features in the temperature and moisture profiles, which had an indirect positive impact on the forecasts of the heavy rainfall in the RMAPS-ST system. However, the deviation in the location of the heavy rainfall center requires future work.

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Development of a hybrid variational-ensemble data assimilation technique for observed lightning tested in a mesoscale model

Nonlinear Processes in Geophysics ◽

10.5194/npg-21-1027-2014 ◽

2014 ◽

Vol 21 (5) ◽

pp. 1027-1041 ◽

Cited By ~ 6

Author(s):

K. Apodaca ◽

M. Zupanski ◽

M. DeMaria ◽

J. A. Knaff ◽

L. D. Grasso

Keyword(s):

Data Assimilation ◽

Prediction Models ◽

Mesoscale Model ◽

Initial Conditions ◽

Positive Impact ◽

Weather Prediction ◽

New Information ◽

Forecasting System ◽

Maximum Likelihood Ensemble Filter ◽

The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Geostationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), and improving initial conditions during several data assimilation cycles. However, the 6 h forecast after the assimilation did not show a clear improvement in terms of root mean square (RMS) errors.

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Long-term Arctic homogenized radiosondes

10.5194/egusphere-egu2020-15418 ◽

2020 ◽

Author(s):

Michael Blaschek ◽

Federico Ambrogi ◽

Leopold Haimberger

Keyword(s):

Weather Prediction ◽

The Arctic ◽

Radiosonde Data ◽

Observation System ◽

Vapour Transport ◽

Water Vapour Transport ◽

First Time ◽

Adjustment Scheme ◽

Stable Measurement

<p>Radiosonde measurements are potentially valuable indicators of upper air climate change because of their unique long-term availability and their high vertical extent and resolution. The radiosonde network, however, is not a long-term stable measurement system, since it was designed for operational use. Changes in the observation system are frequent and surf the purpose of competitive daily weather prediction, but result in more or less clear breakpoints in the observed long-term time series. These artificial biases need to be removed. We apply a bias adjustment scheme for radiosonde temperatures and humidity based on departures from a recent reanalysis, ERA5 potentially back to 1950. Newly digitized and recovered radiosonde data have been used within ERA5 for the first time. We present long-term bias adjustments and trends as preliminary results. In particular, we focus on the water vapour transport into the Arctic as a result of polar amplification and meridional heat exchange.</p>

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Antarctic Low-Tropospheric Humidity Inversions: 10-Yr Climatology

Journal of Climate ◽

10.1175/jcli-d-12-00446.1 ◽

2013 ◽

Vol 26 (14) ◽

pp. 5205-5219 ◽

Cited By ~ 21

Author(s):

Tiina Nygård ◽

Teresa Valkonen ◽

Timo Vihma

Keyword(s):

Water Vapor ◽

Climate Models ◽

Weather Prediction ◽

Temperature Inversion ◽

Statistical Characteristics ◽

The Arctic ◽

Radiosonde Data ◽

Atmospheric Moisture Budget ◽

Temperature Inversions ◽

The Antarctic

Abstract Humidity inversions are nearly permanently present in the coastal Antarctic atmosphere. This is shown based on an investigation of statistical characteristics of humidity inversions at 11 Antarctic coastal stations using radiosonde data from the Integrated Global Radiosonde Archive (IGRA) from 2000 to 2009. The humidity inversion occurrence was highest in winter and spring, and high atmospheric pressure and cloud-free conditions generally increased the occurrence. A typical humidity inversion was less than 200 m deep and 0.2 g kg−1 strong, and a typical humidity profile contained several separate inversion layers. The inversion base height had notable seasonal variations, but generally the humidity inversions were located at higher altitudes than temperature inversions. Roughly half of the humidity inversions were associated with temperature inversions, especially near the surface, and humidity and temperature inversion strengths as well as depths correlated at several stations. On the other hand, approximately 60% of the humidity inversions were accompanied by horizontal advection of water vapor increasing with height, which is also a probable factor supporting humidity inversions. The spatial variability of humidity inversions was linked to the topography and the water vapor content of the air. Compared to previous results for the Arctic, the most striking differences in humidity inversions in the Antarctic were a much higher frequency of occurrence in summer, at least under clear skies, and a reverse seasonal cycle of the inversion height. The results can be used as a baseline for validation of weather prediction and climate models and for studies addressing changes in atmospheric moisture budget in the Antarctic.

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Impact of Coupling with an Ice–Ocean Model on Global Medium-Range NWP Forecast Skill

Monthly Weather Review ◽

10.1175/mwr-d-17-0157.1 ◽

2018 ◽

Vol 146 (4) ◽

pp. 1157-1180 ◽

Cited By ~ 19

Author(s):

Gregory C. Smith ◽

Jean-Marc Bélanger ◽

François Roy ◽

Pierre Pellerin ◽

Hal Ritchie ◽

...

Keyword(s):

Standard Deviation ◽

Large Scale ◽

Weather Forecasting ◽

Positive Impact ◽

Weather Prediction ◽

Ocean Model ◽

Forecast Skill ◽

Medium Range ◽

Forecasting System ◽

The Impact

The importance of coupling between the atmosphere and the ocean for forecasting on time scales of hours to weeks has been demonstrated for a range of physical processes. Here, the authors evaluate the impact of an interactive air–sea coupling between an operational global deterministic medium-range weather forecasting system and an ice–ocean forecasting system. This system was developed in the context of an experimental forecasting system that is now running operationally at the Canadian Centre for Meteorological and Environmental Prediction. The authors show that the most significant impact is found to be associated with a decreased cyclone intensification, with a reduction in the tropical cyclone false alarm ratio. This results in a 15% decrease in standard deviation errors in geopotential height fields for 120-h forecasts in areas of active cyclone development, with commensurate benefits for wind, temperature, and humidity fields. Whereas impacts on surface fields are found locally in the vicinity of cyclone activity, large-scale improvements in the mid-to-upper troposphere are found with positive global implications for forecast skill. Moreover, coupling is found to produce fairly constant reductions in standard deviation error growth for forecast days 1–7 of about 5% over the northern extratropics in July and August and 15% over the tropics in January and February. To the authors’ knowledge, this is the first time a statistically significant positive impact of coupling has been shown in an operational global medium-range deterministic numerical weather prediction framework.

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SINGV – the Convective-Scale Numerical Weather Prediction System for Singapore

ASEAN Journal on Science and Technology for Development ◽

10.29037/ajstd.581 ◽

2019 ◽

Vol 36 (3) ◽

Cited By ~ 4

Author(s):

Xiang-Yu Huang ◽

Dale Barker ◽

Stuart Webster ◽

Anurag Dipankar ◽

Adrian Lock ◽

...

Keyword(s):

Numerical Weather Prediction ◽

Weather Prediction ◽

Extreme Rainfall ◽

Prediction System ◽

Local Data ◽

Weather Forecasts ◽

Numerical Weather ◽

The United Kingdom ◽

Convective Scale ◽

The Impact

Extreme rainfall is one of the primary meteorological hazards in Singapore, as well as elsewhere in the deep tropics, and it can lead to significant local flooding. Since 2013, the Meteorological Service Singapore (MSS) and the United Kingdom Met Office (UKMO) have been collaborating to develop a convective-scale Numerical Weather Prediction (NWP) system, called SINGV. Its primary aim is to provide improved weather forecasts for Singapore and the surrounding region, with a focus on improved short-range prediction of localized heavy rainfall. This paper provides an overview of the SINGV development, the latest NWP capabilities at MSS and some key results of evaluation. The paper describes science advances relevant to the development of any km-scale NWP suitable for the deep tropics and provides some insights into the impact of local data assimilation and utility of ensemble predictions.

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Hybrid variational-ensemble assimilation of lightning observations in a mesoscale model

Nonlinear Processes in Geophysics Discussions ◽

10.5194/npgd-1-917-2014 ◽

2014 ◽

Vol 1 (1) ◽

pp. 917-952

Author(s):

K. Apodaca ◽

M. Zupanski ◽

M. DeMaria ◽

J. A. Knaff ◽

L. D. Grasso

Keyword(s):

Data Assimilation ◽

Prediction Models ◽

Mesoscale Model ◽

Initial Conditions ◽

Positive Impact ◽

Weather Prediction ◽

New Information ◽

Forecasting System ◽

Maximum Likelihood Ensemble Filter ◽

The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Goestationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), improving initial conditions, and partially improving WRF-NMM forecasts during several data assimilation cycles.

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