scholarly journals Impact of Scatterometer Surface Wind Data in the ECMWF Coupled Assimilation System

2016 ◽  
Vol 144 (3) ◽  
pp. 1203-1217 ◽  
Author(s):  
Patrick Laloyaux ◽  
Jean-Noël Thépaut ◽  
Dick Dee
Keyword(s):  
Surface Wind ◽  
Coupled Model ◽  
Coupled System ◽  
Surface Wind Speed ◽  
Wind Data ◽  
Strong Interactions ◽  
Ocean Temperature ◽  
Atmospheric Observations ◽  
The Impact ◽  
Assimilation System

Abstract The European Centre for Medium-Range Weather Forecasts (ECMWF) has developed a coupled assimilation system that ingests simultaneously ocean and atmospheric observations in a coupled ocean–atmosphere model. Employing the coupled model constraint in the analysis implies that assimilation of an ocean observation has immediate impact on the atmospheric state estimate, and, conversely, assimilation of an atmospheric observation affects the ocean state. In this context, observing system experiments have been carried out withholding scatterometer surface wind data over the period September–November 2013. Impacts in the coupled assimilation system have been compared to the uncoupled approach used in ECMWF operations where atmospheric and ocean analyses are computed sequentially. The assimilation of scatterometer data has reduced the background surface wind root-mean-square error in the coupled and uncoupled assimilation systems by 3.7% and 2.5%, respectively. It has been found that the ocean temperature in the mixed layer is improved in the coupled system, while the impact is neutral in the uncoupled system. Further investigations have been conducted over a case of a tropical cyclone when strong interactions between atmospheric wind and ocean temperature occur. Cyclone Phailin in the Bay of Bengal has been selected since the conventional observing system has measured surface wind speed and ocean temperature at a high frequency. In this case study, the coupled assimilation system outperforms the uncoupled approach, being able to better use the scatterometer measurements to estimate the cold wake after the cyclone.

scholarly journals A simple coupled model of the wind-evaporation-SST feedback with a role for stability

2022 ◽  
pp. 1-33
Keyword(s):  
Surface Wind ◽  
Coupled Model ◽  
Coupled System ◽  
Surface Wind Speed ◽  
Boreal Summer ◽  
Boreal Winter ◽  
Wes Feedback ◽  
The Tropics ◽  
Sst Gradient ◽  
Sst Anomalies

Abstract The wind-evaporation-SST (WES) feedback describes a coupled mechanism by which an anomalous meridional sea surface temperature (SST) gradient in the tropics evolves over time. As commonly posed, the (positive) WES feedback depends critically on the atmospheric response to SST anomalies being governed by a process akin to that argued by Lindzen and Nigam (1987), and omits an alternative process by which SST anomalies modulate surface wind speed through vertical momentum mixing as proposed by Wallace et al. (1989) and Hayes et al. (1989). A simple model is developed that captures the essential coupled dynamics of the WES feedback as commonly posed, while also allowing for momentum entrainment in response to evolving SST anomalies. The evolution of the coupled system depends strongly on which effects are enabled in the model. When both effects are accounted for in idealized cases near the equator, the initial anomalous meridional SST gradient grows over a time scale of a few months, but is damped within one year. The sign and magnitude of the WES feedback depend on latitude within the tropics and exhibit hemispheric asymmetry. When constrained by realistic profiles of prevailing zonal wind, the model predicts that the WES feedback near the equator is stronger during boreal winter, while the domain over which it is positive is broader during boreal summer, and that low-frequency climate variability can also modulate the strength and structure of the WES feedback. These insights may aid in the interpretation of coupled climate behavior in observations and more complex models.

scholarly journals Does afforestation deteriorate haze pollution in Beijing–Tianjin–Hebei (BTH), China?

2018 ◽  
Author(s):  
Xin Long ◽  
Naifang Bei ◽  
Jiarui Wu ◽  
Xia Li ◽  
Tian Feng ◽  
...  
Keyword(s):  
Forest Cover ◽  
Fine Particles ◽  
Control Strategies ◽  
Surface Wind ◽  
Forecast Model ◽  
Surface Wind Speed ◽  
Satellite Measurements ◽  
Haze Pollution ◽  
The Impact

Abstract. Although aggressive emission control strategies have been implemented recently in the Beijing–Tianjin–Hebei area (BTH), China, pervasive and persistent haze still frequently engulfs the region during wintertime. Afforestation in BTH, primarily concentrated in the Taihang and Yanshan Mountains, has constituted one of the controversial factors exacerbating the haze pollution due to its slowdown of the surface wind speed. We report here an increasing trend of forest cover in BTH during 2001–2013 based on long-term satellite measurements and the impact of the afforestation on the fine particles (PM2.5) level. Simulations using the Weather Research and Forecast model with chemistry reveal that the afforestation in BTH since 2001 generally deteriorates the haze pollution in BTH to some degree, enhancing PM2.5 concentrations by up to 6 % on average. Complete afforestation or deforestation in the Taihang and Yanshan Mountains would increase or decrease the PM2.5 level within 15 % in BTH. Our model results also suggest that implementing a large ventilation corridor system would not be effective or beneficial to mitigate the haze pollution in Beijing.

scholarly journals Strongly Coupled Assimilation of a Hypothetical Ocean Current Observing Network within a Regional Ocean-Atmosphere Coupled Model: An OSSE Case Study of Typhoon Hato

2021 ◽  
Author(s):  
Luke Phillipson ◽  
Yi Li ◽  
Ralf Toumi
Keyword(s):  
Coupled Model ◽  
Ocean Currents ◽  
Hf Radar ◽  
Ocean Current ◽  
Coastal Current ◽  
Strongly Coupled ◽  
Ocean Atmosphere ◽  
Atmospheric Observations ◽  
Surface Drifters ◽  
The Impact

AbstractThe forecast of tropical cyclone (TC) intensity is a significant challenge. In this study, we showcase the impact of strongly coupled data assimilation with hypothetical ocean currents on analyses and forecasts of Typhoon Hato (2017). Several observation simulation system experiments were undertaken with a regional coupled ocean-atmosphere model. We assimilated combinations of (or individually) a hypothetical coastal current HF radar network, a dense array of drifter floats and minimum sea-level pressure. During the assimilation, instant updates of many important atmospheric variables (winds and pressure) are achieved from the assimilation of ocean current observations using the cross-domain error covariance, significantly improving the track and intensity analysis of Typhoon Hato. As compared to a control experiment (with no assimilation), the error of minimum pressure decreased by up to 13 hPa (4 hPa / 57 % on average). The maximum wind speed error decreased by up to 18 knots (5 knots / 41 % on average). By contrast, weakly coupled implementations cannot match these reductions (10% on average). Although traditional atmospheric observations were not assimilated, such improvements indicate there is considerable potential in assimilating ocean currents from coastal HF radar, and surface drifters within a strongly coupled framework for intense landfalling TCs.

An Investigation of the Stability Dependence of SST-Induced Vertical Mixing over the Ocean in the Operational Met Office Model

2017 ◽  
Vol 30 (1) ◽  
pp. 91-107 ◽  
Author(s):  
Qingtao Song ◽  
Dudley B. Chelton ◽  
Steven K. Esbensen ◽  
Andrew R. Brown
Keyword(s):  
Wind Speed ◽  
Weather Prediction ◽  
Surface Wind ◽  
Vertical Mixing ◽  
Surface Wind Speed ◽  
Satellite Observations ◽  
Sea Surface ◽  
Stable Conditions ◽  
Wide Range ◽  
The Impact

This study presents an assessment of the impact of a March 2006 change in the Met Office operational global numerical weather prediction model through the introduction of a nonlocal momentum mixing scheme. From comparisons with satellite observations of surface wind speed and sea surface temperature (SST), it is concluded that the new parameterization had a relatively minor impact on SST-induced changes in sea surface wind speed in the Met Office model in the September and October 2007 monthly averages over the Agulhas Return Current region considered here. The performance of the new parameterization of vertical mixing was evaluated near the surface layer and further through comparisons with results obtained using a wide range of sensitivity of mixing parameterization to stability in the Weather Research and Forecasting (WRF) Model, which is easily adapted to such sensitivity studies. While the new parameterization of vertical mixing improves the Met Office model response to SST in highly unstable (convective) conditions, it is concluded that significantly enhanced vertical mixing in the neutral to moderately unstable conditions (nondimensional stability [Formula: see text] between 0 and −2) typically found over the ocean is required in order for the model surface wind response to SST to match the satellite observations. Likewise, the reduced mixing in stable conditions in the new parameterization is also relatively small; for the range of the gradient Richardson number typically found over the ocean, the mixing was reduced by a maximum of only 10%, which is too small by more than an order of magnitude to be consistent with the satellite observations.

Alat Ukur Kecepatan Angin Permukaan Air Laut Berbasis Mikrokontroler

2020 ◽  
Vol 2 (2) ◽  
pp. 80-88
Author(s):  
Waluyo Waluyo ◽  
Meli Ruslinar
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Wind Data ◽  
Measuring Instrument ◽  
Ocean Engineering ◽  
Sea Surface Wind Speed ◽  
Wind Speeds ◽  
Sea Surface Wind ◽  
Engineering Modeling

The microcontroller is one technology that is developing so rapidly with various types and functions, one of which is Arduino Uno which can be used as a microcontroller for various functions in the field of electronics technology. This research was conducted at the Laboratory of Ocean Engineering Modeling, Marine and Fisheries Polytechnic of Karawang in March-June 2020. The purpose of this study was to create a microcontroller-based sea surface wind speed measuring instrument. Based on the results of the acquisition of wind data using a fan simulation and natural wind gusts with different wind speeds in the field show a significant tool response. The results of the comparison of data recording between the results of research with the existing wind speed measuring instrument show that there is an average tool error of 3.24%, a relative error of 3.78%, and an instrument accuracy rate of 96.76%. Thus it can be said that the ability of the tool is able to record wind data with high accuracy.

scholarly journals An Idealized Study of Coupled Atmosphere–Ocean 4D-Var in the Presence of Model Error

2016 ◽  
Vol 144 (10) ◽  
pp. 4007-4030 ◽  
Author(s):  
Alison M. Fowler ◽  
Amos S. Lawless
Keyword(s):  
Coupled Model ◽  
Coupled System ◽  
Model Error ◽  
Initial Time ◽  
Coupled Analysis ◽  
Window Length ◽  
Error Growth ◽  
Initial State ◽  
Accurate Forecast ◽  
The Impact

Atmosphere-only and ocean-only variational data assimilation (DA) schemes are able to use window lengths that are optimal for the error growth rate, nonlinearity, and observation density of the respective systems. Typical window lengths are 6–12 h for the atmosphere and 2–10 days for the ocean. However, in the implementation of coupled DA schemes it has been necessary to match the window length of the ocean to that of the atmosphere, which may potentially sacrifice the accuracy of the ocean analysis in order to provide a more balanced coupled state. This paper investigates how extending the window length in the presence of model error affects both the analysis of the coupled state and the initialized forecast when using coupled DA with differing degrees of coupling. Results are illustrated using an idealized single-column model of the coupled atmosphere–ocean system. It is found that the analysis error from an uncoupled DA scheme can be smaller than that from a coupled analysis at the initial time, due to faster error growth in the coupled system. However, this does not necessarily lead to a more accurate forecast due to imbalances in the coupled state. Instead coupled DA is more able to update the initial state to reduce the impact of the model error on the accuracy of the forecast. The effect of model error is potentially most detrimental in the weakly coupled formulation due to the inconsistency between the coupled model used in the outer loop and uncoupled models used in the inner loop.

scholarly journals Assessing a New Coupled Data Assimilation System Based on the Met Office Coupled Atmosphere–Land–Ocean–Sea Ice Model

2015 ◽  
Vol 143 (11) ◽  
pp. 4678-4694 ◽  
Author(s):  
D. J. Lea ◽  
I. Mirouze ◽  
M. J. Martin ◽  
R. R. King ◽  
A. Hines ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Coupled Model ◽  
Background Information ◽  
Ocean Data Assimilation ◽  
Weakly Coupled ◽  
Coupled Data Assimilation ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

Abstract A new coupled data assimilation (DA) system developed with the aim of improving the initialization of coupled forecasts for various time ranges from short range out to seasonal is introduced. The implementation here is based on a “weakly” coupled data assimilation approach whereby the coupled model is used to provide background information for separate ocean–sea ice and atmosphere–land analyses. The increments generated from these separate analyses are then added back into the coupled model. This is different from the existing Met Office system for initializing coupled forecasts, which uses ocean and atmosphere analyses that have been generated independently using the FOAM ocean data assimilation system and NWP atmosphere assimilation systems, respectively. A set of trials has been run to investigate the impact of the weakly coupled data assimilation on the analysis, and on the coupled forecast skill out to 5–10 days. The analyses and forecasts have been assessed by comparing them to observations and by examining differences in the model fields. Encouragingly for this new system, both ocean and atmospheric assessments show the analyses and coupled forecasts produced using coupled DA to be very similar to those produced using separate ocean–atmosphere data assimilation. This work has the benefit of highlighting some aspects on which to focus to improve the coupled DA results. In particular, improving the modeling and data assimilation of the diurnal SST variation and the river runoff should be examined.

scholarly journals Contribution of Dynamic Vegetation Phenology to Decadal Climate Predictability

2014 ◽  
Vol 27 (22) ◽  
pp. 8563-8577 ◽  
Author(s):  
Martina Weiss ◽  
Paul A. Miller ◽  
Bart J. J. M. van den Hurk ◽  
Twan van Noije ◽  
Simona Ştefănescu ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Degrees Of Freedom ◽  
Reference Model ◽  
Coupled Model ◽  
Coupled System ◽  
Cold Bias ◽  
Area Index ◽  
Skill Scores ◽  
The Impact

Abstract In this study, the impact of coupling and initializing the leaf area index from the dynamic vegetation model Lund–Potsdam–Jena General Ecosystem Simulator (LPJ-GUESS) is analyzed on skill of decadal predictions in the fully coupled atmosphere–land–ocean–sea ice model, the European Consortium Earth System Model (EC-Earth). Similar to the impact of initializing the model with the observed oceanic state, initializing the leaf area index (LAI) fields obtained from an offline LPJ-GUESS simulation forced by the observed atmospheric state leads to a systematic drift. A different treatment of the water and soil moisture budget in LPJ-GUESS is a likely cause of this drift. The coupled system reduces the cold bias of the reference model over land by reducing LAI (and the associated evaporative cooling), particularly outside the growing season. The coupling with the interactive vegetation module implies more degrees of freedom in the coupled model, which generates more noise that can mask a portion of the extra signal that is generated. The forecast reliability improves marginally, particularly early in the forecast. Ranked probability skill scores are also improved slightly in most areas analyzed, but the signal is not fully coherent over the forecast interval because of the relatively low number of ensemble members. Methods to remove the LAI drift and allow coupling of other variables probably need to be implemented before significant forecast skill can be expected.

scholarly journals An atmosphere-wave regional coupled model: improving predictions of wave heights and surface winds in the Southern North Sea

2016 ◽  
Author(s):  
Kathrin Wahle ◽  
Joanna Staneva ◽  
Wolfgang Koch ◽  
Luciana Fenoglio-Marc ◽  
Ha T. M. Ho-Hagemann ◽  
...  
Keyword(s):  
North Sea ◽  
German Bight ◽  
Surface Wind ◽  
Model Performance ◽  
Coupled Model ◽  
Wave Model ◽  
Induced Drag ◽  
Wave Heights ◽  
Wave Induced ◽  
The Impact

Abstract. Reduction of wave forecasting errors is a challenge especially in dynamically complicated coastal ocean areas as the southern part of the North Sea area – the German Bight. Coupling of different models is a favoured approach to address this issue as it accounts for the complex interactions of waves, currents and the atmosphere. Here we study the effects of coupling between an atmospheric model and a wind wave model, which in the present study is enabled through an introduction of wave induced drag in the atmosphere model. This, on one side, leads to a reduction of the surface wind speeds, and on the other side, to a reduction of simulated wave heights. The sensitivity of atmospheric parameters such as wind speed, and atmospheric pressure to wave-induced drag, in particular under storm conditions, is studied. Additionally, the impact of the two-way coupling on wave model performance is investigated. The performance of the coupled model system has been demonstrated for extreme events and calm conditions. The results revealed that the effect of coupling results in significant changes in both wind and waves. The simulations are compared to data from in-situ and satellite observations. The results indicate that the two-way coupling improves the agreement between observations and simulations for both wind and wave parameters in comparison to the one-way coupled model. In addition, the errors of the high-resolution German Bight wave model compared to the observations have been significantly reduced in the coupled model. The improved skills resulting from the proposed method justifies its implementations for both operational and climate simulations.

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