scholarly journals Optimizing Mooring Placement to Constrain Southern Ocean Air–Sea Fluxes

2020 ◽  
Vol 37 (8) ◽  
pp. 1365-1385
Author(s):  
Yanzhou Wei ◽  
Sarah T. Gille ◽  
Matthew R. Mazloff ◽  
Veronica Tamsitt ◽  
Sebastiaan Swart ◽  
...  
Keyword(s):  
Southern Ocean ◽  
Numerical Weather Prediction ◽  
High Frequency ◽  
Weather Prediction ◽  
Low Frequency ◽  
Heat Fluxes ◽  
Test Case ◽  
Numerical Weather ◽  
Ocean Observing

AbstractProposals from multiple nations to deploy air–sea flux moorings in the Southern Ocean have raised the question of how to optimize the placement of these moorings in order to maximize their utility, both as contributors to the network of observations assimilated in numerical weather prediction and also as a means to study a broad range of processes driving air–sea fluxes. This study, developed as a contribution to the Southern Ocean Observing System (SOOS), proposes criteria that can be used to determine mooring siting to obtain best estimates of net air–sea heat flux (Qnet). Flux moorings are envisioned as one component of a multiplatform observing system, providing valuable in situ point time series measurements to be used alongside satellite data and observations from autonomous platforms and ships. Assimilating models (e.g., numerical weather prediction and reanalysis products) then offer the ability to synthesize the observing system and map properties between observations. This paper develops a framework for designing mooring array configurations to maximize the independence and utility of observations. As a test case, within the meridional band from 35° to 65°S we select eight mooring sites optimized to explain the largest fraction of the total variance (and thus to ensure the least variance of residual components) in the area south of 20°S. Results yield different optimal mooring sites for low-frequency interannual heat fluxes compared with higher-frequency subseasonal fluxes. With eight moorings, we could explain a maximum of 24.6% of high-frequency Qnet variability or 44.7% of low-frequency Qnet variability.

Evaluation of a high-resolution numerical weather prediction model's simulated clouds using observations from CloudSat, GOES-13 and in situ aircraft

2018 ◽  
Vol 144 (715) ◽  
pp. 1681-1694 ◽  
Author(s):  
Zhipeng Qu ◽  
Howard W. Barker ◽  
Alexei V. Korolev ◽  
Jason A. Milbrandt ◽  
Ivan Heckman ◽  
...  
Keyword(s):  
High Resolution ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather

scholarly journals A Global Ocean Observing System for Measuring Sea Level Atmospheric Pressure: Effects and Impacts on Numerical Weather Prediction

2017 ◽  
Vol 98 (2) ◽  
pp. 231-238 ◽  
Author(s):  
Luca Centurioni ◽  
András Horányi ◽  
Carla Cardinali ◽  
Etienne Charpentier ◽  
Rick Lumpkin
Keyword(s):  
Sea Level ◽  
Surface Pressure ◽  
Numerical Weather Prediction ◽  
Atmospheric Pressure ◽  
Weather Prediction ◽  
Global Ocean ◽  
Numerical Weather ◽  
Observation Impact ◽  
The Impact

Abstract Since 1994 the U.S. Global Drifter Program (GDP) and its international partners cooperating within the Data Buoy Cooperation Panel (DBCP) of the World Meteorological Organization (WMO) and the United Nations Education, Scientific and Cultural Organization (UNESCO) have been deploying drifters equipped with barometers primarily in the extratropical regions of the world’s oceans in support of operational weather forecasting. To date, the impact of the drifter data isolated from other sources has never been studied. This essay quantifies and discusses the effect and the impact of in situ sea level atmospheric pressure (SLP) data from the global drifter array on numerical weather prediction using observing system experiments and forecast sensitivity observation impact studies. The in situ drifter SLP observations are extremely valuable for anchoring the global surface pressure field and significantly contributing to accurate marine weather forecasts, especially in regions where no other in situ observations are available, for example, the Southern Ocean. Furthermore, the forecast sensitivity observation impact analysis indicates that the SLP drifter data are the most valuable per-observation contributor of the Global Observing System (GOS). All these results give evidence that surface pressure observations of drifting buoys are essential ingredients of the GOS and that their quantity, quality, and distribution should be preserved as much as possible in order to avoid any analysis and forecast degradations. The barometer upgrade program offered by the GDP, under which GDP-funded drifters can be equipped with partner-funded accurate air pressure sensors, is a practical example of how the DBCP collaboration is executed. Interested parties are encouraged to contact the GDP to discuss upgrade opportunities.

scholarly journals The influence of non-static sea ice on Antarctic and Southern Ocean numerical weather prediction

2021 ◽  
Author(s):  
Zhaohui Wang ◽  
Alexander D. Fraser ◽  
Phillip Reid ◽  
Richard Coleman ◽  
Siobhan P. O'Farrell
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather

scholarly journals Assessment of the accuracy of snow water equivalent calculation with the use of global numerical weather prediction models and SnoWE snowpack model (by the example of the Kama River basin)

2021 ◽  
Vol 66 (1) ◽  
Author(s):  
Andrey N. Shikhov ◽  
◽  
Evgenii V. Churiulin ◽  
Rinat K. Abdullin ◽  
◽  
...  
Keyword(s):  
River Basin ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Entire Area ◽  
Numerical Weather ◽  
In Situ Data ◽  
Snow Water

The paper discusses the results of snow cover formation and snowmelt modeling in the Kama river basin (S = 507 km2) using two approaches previously developed by the authors. The first one is the SnoWE snowpack model developed at the Hydrometeorological Center of the Russian Federation and used in quasi-operational mode since 2015, and the second is GIS-based empirical technique which was previously implemented for the Kama river basin. Both methods are based on a combination of numerical weather prediction (NWP) models data with operational synoptic observations at the weather stations. The study was performed for the winter seasons 2018/19 and 2019/20. To assess the reliability of simulated snow water equivalent (SWE), we obtained in-situ data from 68 locations (snow survey routes) distributed over the entire area of ​​the river basin. As a result of the study, the main advantages and limitations of two methods for SWE calculation were identified. As for the maximum values of SWE, the root mean square error (RMSE) of simulated SWE ranges from 14% to 28% of the average observed SWE according to in-situ data. It was found, that the SnoWE model more reliably reproduces SWE in the lowland part of the river basin. Simultaneously, SWE was substantially underestimated according to the SnoWE model in the northern and mountainous parts of the basin,. The second method provides a more realistic estimate of the spatial distribution of SWE over the area, as well as a higher accuracy of calculation for its northern part of the river basin. The main drawback of the method is the substantial overestimation of the intensity of snowmelt and snow sublimation. Consequently, the accuracy of SWE calculations sharply decreases in the spring season. Wherein, SWE calculation accuracy in the winter season 2019/20 was substantially lower than in 2018/19 due to frequent thaws.

scholarly journals Comparisons of Diurnal Variations of Land Surface Temperatures from Numerical Weather Prediction Analyses, Infrared Satellite Estimates and In Situ Measurements

2020 ◽  
Vol 12 (3) ◽  
pp. 583 ◽  
Author(s):  
Xiaoni Wang ◽  
Catherine Prigent
Keyword(s):  
Numerical Weather Prediction ◽  
Land Surface ◽  
Weather Prediction ◽  
In Situ Measurements ◽  
The Other ◽  
Surface Model ◽  
Night Time ◽  
Barren Land ◽  
Numerical Weather

This study evaluates the diurnal cycle of Land Surface Temperature (LST) from Numerical Weather Prediction (NWP) reanalyses (ECMWF ERA5 and ERA Interim), as well as from infrared satellite estimates (ISCCP and SEVIRI/METEOSAT), with in situ measurements. Data covering a full seasonal cycle in 2010 are studied. Careful collocations and cloud filtering are applied. We first compare the reanalysis and satellite products at continental and regional scales, and then we concentrate on comparisons with the in situ observations, under a large variety of environments. SEVIRI shows better agreement with the in situ measurements than the other products, with bias often less than ±2K and correlation of 0.99. Over snow or arid surface, ISCCP tends to have more systematic errors than the other products. ERA5 agrees better to the in situ over barren land than ERA Interim, particularly at night time, thanks to the new surface model. However, over vegetated surfaces, both reanalyses tend to have higher/lower temperature at night/day time than the in situ measurements, probably related to the surface processes and its interactions with atmosphere in the NWP model.

scholarly journals Improving High-Impact Numerical Weather Prediction with Lidar and Drone Observations

2020 ◽  
Vol 101 (7) ◽  
pp. E1036-E1051 ◽  
Author(s):  
Daniel Leuenberger ◽  
Alexander Haefele ◽  
Nadja Omanovic ◽  
Martin Fengler ◽  
Giovanni Martucci ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Positive Impact ◽  
Weather Prediction ◽  
Lower Troposphere ◽  
High Impact ◽  
Vertical Resolution ◽  
Numerical Weather ◽  
Temperature And Humidity ◽  
Ground Based Lidar

Abstract The current atmospheric observing systems fail to provide a satisfactory amount of spatially and temporally resolved observations of temperature and humidity in the planetary boundary layer (PBL) despite their potential positive impact on numerical weather prediction (NWP). This is particularly critical for humidity, which exhibits a very high variability in space and time or for the vertical distribution of temperature, determining the atmosphere’s stability. Novel ground-based lidar remote sensing technologies and in situ measurements from unmanned aerial vehicles can fill this observational gap, but operational maturity was so far lacking. Only recently, commercial lidar systems for temperature and humidity profiling in the lower troposphere and automated observations on board of drones have become available. Raman lidar can provide profiles of temperature and humidity with high temporal and vertical resolution in the troposphere. Drones can provide high-quality in situ observations of various meteorological variables with high temporal and vertical resolution, but flights are complicated in high-wind situations, icing conditions, and can be restricted by aviation activity. Both observation systems have shown to considerably improve analyses and forecasts of high-impact weather, such as thunderstorms and fog in an operational, convective-scale NWP framework. The results of this study demonstrate the necessity for and the value of additional, high-frequency PBL observations for NWP and how lidar and drone observations can fill the gap in the current operational observing system.

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