Evaluation of Areal Monthly Average Precipitation Estimates from MERRA2 and ERA5 Reanalysis in a Colombian Caribbean Basin

Global reanalysis dataset estimations of climate variables constitute an alternative for overcoming data scarcity associated with sparsely and unevenly distributed hydrometeorological networks often found in developing countries. However, reanalysis datasets require detailed validation to determine their accuracy and reliability. This paper evaluates the performance of MERRA2 and ERA5 regarding their monthly rainfall products, comparing their areal precipitation averages with estimates based on ground measurement records from 49 rain gauges managed by the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM) and the Thiessen polygons method in the Sinu River basin, Colombia. The performance metrics employed in this research are the correlation coefficient, the bias, the normalized root mean square error (NRMSE), and the Nash–Sutcliffe efficiency (NSE). The results show that ERA5 generally outperforms MERRA2 in the study area. However, both reanalyses consistently overestimate the monthly averages calculated from IDEAM records at all time and spatial scales. The negative NSE values indicate that historical monthly averages from IDEAM records are better predictors than both MERRA2 and ERA5 rainfall products.

Calculating the vertical column density of O₄ from surface values of pressure, temperature and relative humidity

We present a formalism that relates the vertical column density (VCD) of the oxygen collision complex O2-O2 (denoted as O4 below) to surface (2 m) values of temperature and pressure, based on physical laws. In addition, we propose an empirical modification which also accounts for surface relative humidity (RH). This allows for simple and quick calculation of the O4 VCD without the need for constructing full vertical profiles. The parameterization reproduces the real O4 VCD, as derived from vertically integrated profiles, within −0.9 % ± 1.0 % for WRF simulations around Germany, 0.1 % ± 1.2 % for global reanalysis data (ERA5), and −0.4 % ± 1.4 % for GRUAN radiosonde measurements around the world. When applied to measured surface values, uncertainties of 1 K, 1 hPa, and 16 % for temperature, pressure, and RH correspond to relative uncertainties of the O4 VCD of 0.3 %, 0.2 %, and 1 %, respectively. The proposed parameterization thus provides a simple and accurate formula for the calculation of the O4 VCD which is expected to be useful in particular for MAX-DOAS applications.

A High Resolution Coupled Ocean-atmosphere Simulation of the Regional Climate Over Central America

This study analyzes a relatively high resolution (15km grid spacing), regional coupled ocean-atmosphere simulation configured over Central America. The simulation is forced with global atmospheric and oceanic reanalysis for a period of 25 years (1986-2010). The spatial resolution and the time period of the Regional Climate Model (RCM) simulation are both unprecedented for the region. The highlights of the RCM simulation include the verifiable seasonal cycle of mesoscale features like the low level jets, the mid-summer drought and the seasonal tropical cyclone activity both in the Pacific and in the Atlantic Oceans. Similarly, the seasonal cycle of the robust surface ocean currents in the eastern Pacific and the Costa Rica Dome is also well captured in the RCM simulation. The RCM simulation also resolves the seasonal cycle of the Panama-Colombia Gyre, the Gulf of Papagayo and the Gulf of Tehuantepec Gyre. In many instances we find the RCM improves upon the global reanalysis forcing the simulation, indicating the potential value of dynamic downscaling. Furthermore, the co-evolving components of the atmosphere and ocean in the RCM is an added benefit to the atmosphere only and ocean only global reanalysis forcing the simulation. However, the model displays significant biases that manifest in precipitation, precipitable water, SST and winds which could potentially be improved.

The new Italian regional reanalysis SPHERA: benefits of the convection-permitting resolution in detecting severe-weather events

<p>In recent years the interest towards the development of limited-area atmospheric reanalysis datasets has been growing more and more. Regional reanalyses in fact, as a consequence of the restricted domain that they cover, provide a data distribution displaced on a much finer grid compared to a coarser global dataset. This permits to better resolve those patterns related to rapid and high-impact weather events, first and foremost convection. Furthermore, with a finer horizontal resolution, a consistent increase in the level of detail in the description of the orography is also gained, that is a crucial point to achieve especially in a very complex territory such as Italy. This study presents the first application of the novel regional reanalysis dataset developed at ARPAE-SIMC: the High rEsolution ReAnalysis over Italy (SPHERA). SPHERA is a high-resolution convection-permitting reanalysis over the Italian domain and the surrounding seas covering 25 years, from 1995 to 2020, at hourly temporal frequency. SPHERA is based on the non-hydrostatic limited-area model COSMO, and produced by a dynamical downscaling of the global reanalysis ERA5, developed at ECMWF. A nudging data assimilation scheme is applied in order to steer the model outcomes towards the surface and upper-air observations. All the available conventional observations have been used.</p><p>The added value of SPHERA in representing severe-weather and convective events is evident from its preliminar validation, which was performed on the multidecadal period against various datasets of surface observations, joined with the comparison against the global reanalysis ERA5. In fact, a clear advantage of SPHERA on its driver ERA5 is found for the detection of events with moderate to intense daily and sub-daily rainfalls, which are characterized by a strong seasonal and geographical component, that is further investigated. We report also the preliminary sensitivity analysis on the dimension of the box used to operate the upscaling for the validation of SPHERA, a process necessary to reduce the errors caused by geographical mismatches between observed and simulated events localizations, which are particularly frequent in case of strongly-localized and rapid processes. Furthermore, in order to give a quantitative evaluation of the performance of the new reanalysis in particular conditions, the results of the simulations for specific case studies involving the occurrence of severe-precipitation events in recent years was performed, focusing on events having different dynamical genesis, but interrelated by the important damages they caused. From this analysis, for which also a comparison with other regional reanalyses is performed, the advantage of SPHERA in representing the most intense rainfall occurrences, in terms of location, intensity and timing, clearly emerges.</p>

Evaluation of Precipitation in the Chinese Regional Reanalysis Using Satellite Estimates, Gauge-Based Observations and Global Reanalysis

Two high-resolution Chinese regional reanalysis (CNRR) datasets at a resolution of 18 km during the period of 1998–2009 are generated by Gridpoint Statistical Interpolation (GSI) data assimilation system and spectral nudging (SN) method. The precipitation from CNRR is comprehensively evaluated against the observational datasets and global reanalysis ERA5 over East-Asia. The climatology mean, seasonal variability, extreme events, and summer diurnal cycle of precipitation are analyzed. Results show that CNRR reasonably reproduces the observed characteristics of rainfall, although some biases exist. The spatial distribution of climatology mean precipitation is well simulated by CNRR, while overestimation exists especially on the west side of Tibetan-Plateau (TP). CNRR reproduces the unimodal feature of the annual cycle with overestimations of summer precipitation, and well produces the probability of light and moderate rainfall but tend to overestimate heavy and extreme precipitation over most regions in China. The overall spatial distribution of extreme precipitation indices can be captured by CNRR. The diurnal cycle of summer precipitation, as well as the amplitude of diurnal cycle, are better reproduced by CNRR-GSI, capturing eastward propagation of diurnal phase from TP along the Yangtze River. CNRR-GSI generally outperforms CNRR-SN over most regions of China except in reproducing heavy and extreme rainfall in the Yangtze River Basin (YRB) and South China (SC) regions. CNRR-GSI shows comparable results with the latest ERA5 and outperforms it in simulating the diurnal cycle of precipitation. This dataset can be considered as a reliable source for precipitation related applications.

Assessment of Temperature and Specific Humidity Inversions and Their Relationships in Three Global Reanalysis Products over the Arctic Ocean

Characteristics of temperature inversions (TIs) and specific humidity inversions (SHIs) and their relationships in three of the latest global reanalyses—the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-I), the Japanese 55-year Reanalysis (JRA-55), and the ERA5—are assessed against in situ radiosonde (RS) measurements from two expeditions over the Arctic Ocean. All reanalyses tend to detect many fewer TI and SHI occurrences, together with much less common multiple TIs and SHIs per profile than are seen in the RS data in summer 2008, winter 2015, and spring 2015. The reanalyses generally depict well the relationships among TI characteristics seen in RS data, except for the TIs below 400 m in summer, as well as above 1000 m in summer and winter. The depth is simulated worst by the reanalyses among the SHI characteristics, which may result from its sensitivity to the uncertainties in specific humidity in the reanalyses. The strongest TI per profile in RS data exhibits more robust dependency on surface conditions than the strongest SHI per profile, and the former is better presented by the reanalyses than the latter. Furthermore, all reanalyses have difficulties simulating the relationships between TIs and SHIs, together with the correlations between the simultaneous inversions. The accuracy and vertical resolution in the reanalyses are both important to properly capture occurrence and characteristics of the Arctic inversions. In general, ERA5 performs better than ERA-I and JRA-55 in depicting the relationships among the TIs. However, the representation of SHIs is more challenging than TIs in all reanalyses over the Arctic Ocean.