Lagrangian gravity wave spectra in the lower stratosphere of current (re)analyses

Due to their increasing spatial resolution, numerical weather prediction (NWP) models and the associated analyses resolve a growing fraction of the gravity wave (GW) spectrum. However, it is unclear how well this “resolved” part of the spectrum truly compares to the actual atmospheric variability. In particular, the Lagrangian variability, relevant, for example, to atmospheric dispersion and to microphysical modeling in the upper troposphere–lower stratosphere (UTLS), has not yet been documented in recent products. To address this shortcoming, this paper presents an assessment of the GW spectrum as a function of the intrinsic (air parcel following) frequency in recent (re)analyses (ERA-Interim, ERA5, the ECMWF operational analysis and MERRA-2). Long-duration, quasi-Lagrangian balloon observations in the equatorial and Antarctic lower stratosphere are used as a reference for the atmospheric spectrum and are compared to synthetic balloon observations along trajectories calculated using the wind and temperature fields of the reanalyses. Overall, the reanalyses represent realistic features of the spectrum, notably the spectral gap between planetary and gravity waves and a peak in horizontal kinetic energy associated with inertial waves near the Coriolis frequency f in the polar region. In the tropics, they represent the slope of the spectrum at low frequency. However, the variability is generally underestimated even in the low-frequency portion of the spectrum. In particular, the near-inertial peak, although present in the reanalyses, has a reduced magnitude compared to balloon observations. We compare the observed and modeled variabilities of temperature, zonal momentum flux and vertical wind speed, which are related to low-, mid- and high-frequency waves, respectively. The probability density function (PDF) distributions have similar shapes but show increasing disagreement with increasing intrinsic frequency. Since at those altitudes they are mainly caused by gravity waves, we also compare the geographic distribution of vertical wind fluctuations in the different products, which emphasizes the increase of both GW variance and intermittency with horizontal resolution. Finally, we quantify the fraction of resolved variability and its dependency on model resolution for the different variables. In all (re)analysis products, a significant part of the variability is still missing, especially at high frequencies, and should hence be parameterized. Among the two polar balloon datasets used, one was broadcast on the Global Telecommunication System for assimilation in NWP models, while the other consists of independent observations (unassimilated in the reanalyses). Comparing the Lagrangian spectra between the two campaigns shows that the (re)analyses are largely influenced by balloon data assimilation, which especially enhances the variance at low GW frequency.

Assessment on the wind hazard of tropical cyclones over the Northwest Pacific basin with parametric wind field model

<p>Strong winds over the sea surface induced by tropical cyclones (TCs) of Northwest Pacific (NWP) basin have been posing great threats to maritime activities, and quantitative assessment on its hazard intensity is of great importance. In the past, most studies focused on the modeling of winds over the land and areas of major island areas numerically or statistically. However, there is no systematic assessment of TC wind hazard over the NWP basin with long-term wind time series based on windfield modeling of historical TC events. In this study, the footprints of historical TC events during 1949~2019 were modeled based on the parametric models developed in previous studies, which simulate the winds of both gradient layer and planetary boundary layer. The historical TC track data were obtained from the China Meteorological Administration, and the wind records from the Global Telecommunication System (GTS) data were used for the calibration and validation of the models. The spatial resolution of the modeling output is 1km for winds over the sea surface. In order to reflect wind speed heterogeneity over the land of small islands, the wind speeds were modeled with 90-meter resolution by considering local terrain effects and roughness heights of islands, derived from 90m SRTM DEM data and 30m land-used data. Based on the simulated wind footprints of the 2384 TC events during 1949~2019, the relationships between wind intensity and frequency of each modeling pixel were analyzed and fitted with General Extreme Value (GEV) distribution. A series of wind hazard maps, including wind speeds for return periods of 5a, 10a, 20a, 50a and 100a, and the exceedance probabilities of wind scales from 10 to 17, etc were produced. These wind hazard maps are useful to the management of TC disaster risks in the NWP basin.</p>

New BioGeoChemical products provided by the Copernicus Marine Service

<p>The Copernicus Marine service is a “one-stop-shop” providing freely available operational data on the state of the marine environment for use by marine managers, advisors, and scientists, as well as intermediate and end users in marine businesses and operations. The Copernicus Marine service offers operationally updated and state-of-the-art products that are well documented and transparent. The European Commission’s long-term commitment to the Copernicus program offers long-term visibility and stability of the Copernicus Marine products. Furthermore, Copernicus Marine offers a dedicated service desk, in addition to training sessions and workshops.</p><p>Here, we present the in situ biogeochemical data products distributed by the Copernicus Marine System since 2018. It offers available data of chlorophyll-<em>a</em>, oxygen, and nutrients collected across the globe. These products integrate observation aggregated from the Regional EuroGOOS consortium (Arctic-ROOS, BOOS, NOOS, IBI-ROOS, MONGOOS) and Black Sea GOOS as well as from SeaDataNet2 National Data Centers (NODCs) and JCOMM global systems (Argo, GOSUD, OceanSITES, GTSPP, DBCP) and the Global telecommunication system (GTS) used by the Met Offices.</p><p>The in situ Near Real Time biogeochemical product is updated every month whereas the reprocessed product is updated two times per year. Products are delivered on NetCDF4 format compliant with the CF1.7 standard and well-documented quality control procedures.</p>

Lagrangian Gravity Wave spectra in the lower stratosphere of current (re)analyses

Due to their increasing spatial resolution, numerical weather prediction (NWP) models and the associated analyses resolve a growing fraction of the gravity wave (GW) spectrum. However, it is unclear how well this resolved part of the spectrum actually compares to the actual atmospheric variability. In particular, the Lagrangian variability, relevant, e.g., to atmospheric dispersion and to microphysical modeling in the Upper Troposphere-Lower Stratosphere (UTLS), has not yet been documented in recent products. To address this shortcoming, this paper presents an assessment of the GW spectrum as a function of the intrinsic (air parcel following) frequency in recent (re)analyses (ERA-interim, ERA5, the ECMWF operational analysis, MERRA-2 and JRA-55). Long-duration, quasi-Lagrangian balloon observations in the equatorial and Antarctic lower stratosphere are used as a reference for the atmospheric spectrum and compared to synthetic balloon observations along trajectories calculated using the wind and temperature fields of the reanalyses. Overall, the reanalyses represent realistic features of the spectrum, notably the spectral gap between planetary and gravity waves and a peak in horizontal kinetic energy associated with inertial waves near f in the polar region. In the tropics, they represent the slope of the spectrum at low frequency. However, the variability is generally underestimated, even in the low-frequency portion of the spectrum. In particular, the near-inertial peak, although present in the reanalyses, has a much reduced magnitude compared to balloon observations. We compare the variability of temperature, momentum flux and vertical wind speed, which are related to low, mid and high frequency waves, respectively. The distributions (PDFs) have similar shapes, but show increasing disagreement with increasing intrinsic frequency. Since at those altitudes they are mainly caused by gravity waves, we also compare the geographic distribution of vertical wind fluctuations in the different products, which emphasizes the increase of both GW variance and intermittency with horizontal resolution. Finally, we quantify the fraction of resolved variability and its dependency on model resolution for the different variables. In all (re)analyses products, a significant part of the variability is still missing and should hence be parameterized, in particular at high intrinsic frequency. Among the two polar balloon datasets used, one was broadcast on the global telecommunication system for assimilation in analyses while the other is made of independent observations (unassimilated in the reanalyses). Comparing the Lagrangian spectra between the two campaigns shows that they are largely influenced by balloon data assimilation, which especially enhances the variance at low frequency.

The Impact of Low Latency Satellite Sounder Observations on Local Severe Storm Forecasts in Regional NWP

The forecasts of local severe storms (LSS) are highly dependent on how well the pre-convection environment is characterized in the numerical weather prediction (NWP) model analysis. The usefulness of the forecasts is highly dependent on how frequently the forecast is updated. Therefore, the data latency is critical for assimilation into regional NWP models for it to be able to assimilate more data within the data cut-off window. These low latency data can be obtained through direct broadcast sites and direct receiving systems. Observing system experiments (OSE) were performed to study the impact of data latency on the LSS forecasts. The experiments assimilated all existing observations including conventional data (from the global telecommunication system, GTS) and satellite sounder radiance data (AMSU-A (The Advanced Microwave Sounding Unit-A), ATMS (Advanced Technology Microwave Sounder), CrIS (Cross-track Infrared Sounder), and IASI (Infrared Atmospheric Sounding Interferometer)). They were carried out in a nested domain with a horizontal resolution of 9 km and 3 km in the weather research and forecasting (WRF) model. The forecast quality scores of the LSS precipitation forecasts were calculated and compared with different data cut-off widows to evaluate the impact of data latency. The results showed that low latency can lead to an improved and positive impact on precipitation and other forecasts, which indicates the potential application of LEO direct broadcast (DB) data in a high-resolution regional NWP for LSS forecasts.

Impact of additional AMDAR data in the AROME-France model during May 2017

From 1 May 2017 until 15 June 2017, the E-AMDAR operational service from EUMETNET disseminated more commercial aircraft data than usual on the Global Telecommunication System (GTS). Météo-France specifically requested the implementation of such a trial. It lead to an increase in the number of aircraft data over France, especially vertical profiles (ascents and descents). Though Météo-France routinely buys additional data with respect to the basic E-AMDAR service, this trial aimed at assessing the potential of French airlines to produce further data in collaboration with E-AMDAR and yield an observation network as dense as possible. This was the opportunity to check the impact of these additional data on forecast skill scores of the limited area and convective scale model AROME-France. A data denial experiment (OSE) was carried out on May 2017, by removing E-AMDAR profiles (about 14 % of data) to mimic the routine observing system. The reference was the operational AROME-France 3D-Var that assimilated all extra data in real-time. However, no dedicated flag allowed to distinguish supplementary data from routine ones. Therefore, a necessary step of the experimental methodology was to identify which data profile could be considered as supplementary. The examination of forecast skill scores from the denial experiment showed that the impact of the removal of the additional observations is rather small and mixed, depending upon the parameter of interest, the atmospheric level, and the forecast range. The case studies done did not exhibit any particular additional skill for the suite with augmented observations. The experimental set-up is described and the results are discussed on the basis of forecast scores, including precipitation scores. Finally, a number of recommendations are given for a more optimal assimilation of AMDAR data in the AROME-France model.

Evaluation of Doppler radar and GTS data assimilation for NWP rainfall prediction of an extreme summer storm in northern China: from the hydrological perspective

Data assimilation is an effective tool in improving high-resolution rainfall of the numerical weather prediction (NWP) systems which always fails in providing satisfactory rainfall products for hydrological use. The aim of this study is to explore the potential effects of assimilating different sources of observations, i.e., the Doppler weather radar and the Global Telecommunication System (GTS) data, in improving the mesoscale NWP rainfall products. A 24 h summer storm occurring over the Beijing–Tianjin–Hebei region of northern China on 21 July 2012 is selected as a case study. The Weather Research and Forecasting (WRF) Model is used to obtain 3 km rainfall forecasts, and the observations are assimilated using the three-dimensional variational (3DVar) data assimilation method. Eleven data assimilation modes are designed for assimilating different combinations of observations in the two nested domains of the WRF model. Both the rainfall accumulative amount and its distribution in space and time are examined for the forecasting results with and without data assimilation. The results show that data assimilation can effectively help improve the WRF rainfall forecasts, which is of great importance for hydrologic applications through the rainfall–runoff transformation process. Both the radar reflectivity and the GTS data are good choices for assimilation in improving the rainfall products, whereas special attention should be paid to assimilating radial velocity where unsatisfactory results are always found. The assimilation of the GTS data in the coarser domain has positive effects on the radar data assimilation in the finer domain, which can make the rainfall forecasts more accurate than assimilating the radar data alone. It is also found that the assimilation of more observations cannot guarantee further improvement of the rainfall products, whereas the effective information contained in the assimilated data is of more importance than the data quantity. Potential improvements of data assimilation in improving the NWP rainfall products are discussed and suggestions are further made.

Evaluation of Doppler radar and GTS Data Assimilation for NWP Rainfall Prediction of an Extreme Summer Storm in Northern China: from the Hydrological Perspective

Data assimilation is an effective tool in improving high-resolution rainfall of the numerical weather prediction (NWP) systems which always fails in providing satisfactory rainfall products for hydrological use. The aim of this study is to explore the potential effects of assimilating different sources of observations from the Doppler weather radar and the Global Telecommunication System (GTS) in improving the mesoscale NWP rainfall products. A 24 h summer storm occurring over the Beijing-Tianjin-Hebei region of northern China on 21 July 2012 is selected in this study. The Weather Research and Forecasting (WRF) model is used to obtain 3 km rainfall forecasts, and the observations are assimilated using the three-dimensional variational (3D-Var) data assimilation method. Eleven data assimilation modes are designed for assimilating different combinations of observations in the two nested domains of the WRF model. Results show that the assimilation can largely improve the WRF rainfall products especially the accumulative process of rainfall, which is of great importance for hydrologic applications through the rainfall-runoff transformation process. Both radar reflectivity and GTS data are good choices for assimilation in improving the rainfall products, whereas special attentions should be paid for assimilating radial velocity where unsatisfactory results are always found. Simultaneously assimilating GTS and radar data always perform better than assimilating radar data alone. The inclusion of GTS data in the nested domains when radar reflectivity and radial velocity are assimilated in the innermost domain show the best results among all the 11 assimilation modes. The assimilation efficiency of the GTS data is higher than both radar reflectivity and radial velocity considering the number of data assimilated and its effect. It is also found that the assimilation of more observations cannot guarantee further improvement of the rainfall products, whereas the effective information contained in the assimilated data is of more importance than the data quantity. Potential improvements of data assimilation in improving the NWP rainfall products are discussed and suggestions are also made.

Chronological Development of Terrestrial Mean Precipitation

Over 150 years of investigations into global terrestrial precipitation are revisited to reveal how researchers estimated annual means from in situ observations before the age of digitization. After introducing early regional efforts to measure precipitation, the pioneering estimates of terrestrial mean precipitation from the late nineteenth and early twentieth centuries are compared to successive estimates, including those using the latest gridded precipitation datasets available. The investigation reveals that the range of the early estimates is comparable to the interannual variation in terrestrial mean precipitation derived from the latest Climatic Research Unit (CRU) dataset. In-depth revisions of the estimates were infrequent up to the 1970s, due in part to difficulty obtaining and maintaining up-to-date datasets with global coverage. This point is illustrated in a “family tree” that identifies the key publications that subsequent authors referenced, sometimes decades after the original publication. Significant efforts to collate global observations facilitated new investigations and improved data exchange, for example, in the International Hydrological Decade (1965–74) and following the establishment of the Global Telecommunication System under the World Weather Watch Programme of the World Meteorological Organization. Also in the 1970s were the first attempts to adjust in situ observations on a global scale to account for gauge undercatch, and this had a noticeable impact on mean annual estimates. There remains no single satisfactory approach to gauge bias adjustment. Echoing the repeated message of past researchers, today’s authors cite poor spatial coverage, temporal inhomogeneity, and inadequate sharing of in situ observations as the key obstacles to obtaining more accurate estimates of terrestrial mean precipitation.

Three-model ensemble wind prediction in southern Italy

Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013) three-model ensemble (TME) experiment for wind prediction is considered. The models employed, run operationally at National Research Council – Institute of Atmospheric Sciences and Climate (CNR-ISAC), are RAMS (Regional Atmospheric Modelling System), BOLAM (BOlogna Limited Area Model), and MOLOCH (MOdello LOCale in H coordinates). The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System). Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System) of the ECMWF (European Centre for Medium-Range Weather Forecast) for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.