Performance Assessment of GPM IMERG Products at Different Time Resolutions, Climatic Areas and Topographic Conditions in Catalonia

Quantitative Precipitation Estimates (QPEs) from the Integrated Multisatellite Retrievals for GPM (IMERG) provide crucial information about the spatio-temporal distribution of precipitation in semiarid regions with complex orography, such as Catalonia (NE Spain). The network of automatic weather stations of the Meteorological Service of Catalonia is used to assess the performance of three IMERG products (Early, Late and Final) at different time scales, ranging from yearly to sub-daily periods. The analysis at a half-hourly scale also considered three different orographic features (valley, flat and ridgetop), diverse climatic conditions (BSk, Csa, Cf and Df) and five categories related to rainfall intensity (light, moderate, intense, very intense and torrential). While IMERG_E and IMERG_L overestimate precipitation, IMERG_F reduces the error at all temporal scales. However, the calibration to which a Final run is subjected causes underestimation regardless in some areas, such as the Pyrenees mountains. The proportion of false alarms is a problem for IMERG, especially during the summer, mainly associated with the detection of false precipitation in the form of light rainfall. At sub-daily scales, IMERG showed high bias and very low correlation values, indicating the remaining challenge for satellite sensors to estimate precipitation at high temporal resolution. This behaviour was more evident in flat areas and cold semi-arid climates, wherein overestimates of more than 30% were found. In contrast, rainfall classified as very heavy and torrential showed significant underestimates, higher than 80%, reflecting the inability of IMERG to detect extreme sub-daily precipitation events

Observational and Numerical Simulation Study of a Sequence of Eight Atmospheric Density Currents in Northern Spain

A sequence of eight atmospheric density current fronts occurred in consecutive days are identified and analyzed using micrometeorological time series and numerical simulations. Observations were collected in the context of the INTERCLE project, which took place from September 2002 to November 2003 at the CIBA (Research Centre for the Lower Atmosphere) site located over the northern Spanish plateau. Numerical simulations used the Weather Research and Forecast (WRF) model with fine horizontal resolution (1 km). Both observations and simulations agree that the arrival of the density currents are characterized by a sharp change in temperature, wind velocity, wind direction and specific humidity and a source of intermittent turbulence. However, comparison between model and observations shows that the model predicts the intrusion of the density currents earlier than is observed. In addition, wavelet techniques applied to the data help distinguish the different scales present in the events, and therefore can reveal traces of gravity waves induced by the arrival of the density currents

WRF-LES Simulation of the Boundary Layer Turbulent Processes during the BLLAST Campaign

A real case long-term nested large eddy simulation (LES) of 25-day duration is performed using the WRF-LES modelling system, with a maximum horizontal grid resolution of 111 m, in order to explore the ability of the model to reproduce the turbulence magnitudes within the first tens of metres of the boundary layer. Sonic anemometer measurements from a 60-m tower installed during the Boundary Layer Late Afternoon and Sunset Turbulence (BLLAST) field campaign are used for verification, which is focused on the turbulent magnitudes in order to assess the success and limitations in resolving turbulent flow characteristics. The mesoscale and LES simulations reproduce the wind speed and direction fairly well, but only LES is able to reproduce the energy of eddies with lifetimes shorter than a few hours. The turbulent kinetic energy in LES simulation is generally underestimated during the daytime, mainly due to a vertical velocity standard deviation that is too low. The turbulent heat flux is misrepresented in the model, probably due to the inaccuracy of the sub-grid scheme

A new methodology to characterise the radar bright band using doppler spectral moments from vertically pointing radar observations

The detection and characterisation of the radar Bright Band (BB) are essential for many applications of weather radar quantitative precipitation estimates, such as heavy rainfall surveillance, hydrological modelling or numerical weather prediction data assimilation. This study presents a new technique to detect the radar BB levels (top, peak and bottom) for Doppler radar spectral moments from the vertically pointing radars applied here to a K-band radar, the MRR-Pro (Micro Rain Radar). The methodology includes signal and noise detection and dealiasing schemes to provide realistic vertical Doppler velocities of precipitating hydrometeors, subsequent calculation of Doppler moments and associated parameters and BB detection and characterisation. Retrieved BB properties are compared with the melting level provided by the MRR-Pro manufacturer software and also with the 0 °C levels for both dry-bulb temperature (freezing level) and wet-bulb temperature from co-located radio soundings in 39 days. In addition, a co-located Parsivel disdrometer is used to analyse the equivalent reflectivity of the lowest radar height bins confirming consistent results of the new signal and noise detection scheme. The processing methodology is coded in a Python program called RaProM-Pro which is freely available in the GitHub repository

Multiple characteristics of precipitation inferred from wind profiler radar Doppler spectra

A methodology to process radar wind profiler Doppler spectra is presented and implemented for an UHF Degreane PCL1300 system. First, double peak signal detection is conducted at each height level and, then, vertical continuity checks for each radar beam ensure physically consistent measurements. Second, horizontal and vertical wind, kinetic energy flux components, Doppler moments, and different precipitation-related variables are computed. The latter include a new precipitation type estimate, which considers rain, snow, and mixed types, and, finally, specific variables for liquid precipitation, including drop size distribution parameters, liquid water content and rainfall rate. The methodology is illustrated with a 48 h precipitation event, recorded during the Cerdanya-2017 field campaign, carried out in the Eastern Pyrenees. Verification is performed with a previously existing process for wind profiler data regarding wind components, plus precipitation estimates derived from Micro Rain Radar and disdrometer observations. The results indicated that the new methodology produced comparable estimates of wind components to the previous methodology (Bias < 0.1 m/s, RMSE ˜ 1.1 m/s), and was skilled in determining precipitation type when comparing the lowest estimate of disdrometer data for snow and rain, but did not correctly identify mixed precipitation cases. The proposed methodology, called UBWPP, is available at the GitHub repository.Peer ReviewedPostprint (published version

Monitoring storm evolution using a high-density seismic network

Data acquired by a dense seismic network deployed in the Cerdanya basin (Eastern Pyrenees) is used to track the temporal and spatial evolution of meteorological events such as rainfall episodes or thunderstorms. Comparing seismic and meteorological data, we show that for frequencies above 40 Hz, the dominant source of seismic noise is rainfall and hence the amplitude of the seismic data can be used as a proxy of rainfall. The interstation distance of 1.5 km provides an unprecedented spatial resolution of the evolution of rainfall episodes along the basin. Two specific episodes, one dominated by stratiform rain and the second one dominated by convective rain, are analyzed in detail, using high resolution disdrometer data from a meteorological site near one of the seismic instruments. Seismic amplitude variations follow a similar evolution to radar reflectivity values, but in some stratiform precipitation cases, it differs from the radar-derived precipitation estimates in this region of abrupt topography, where radar may suffer antenna beam blockage. Hence, we demonstrate the added value of seismic data to complement other sources of information such as rain-gauge or weather radar observations to describe the evolution of ground-level rainfall fields at high spatial and temporal resolution. The seismic power and the rainfall intensity have an exponential relationship and the periods with larger seismic power are coincident. The time intervals with rain drops diameters exceeding 3.5 mm do not result in increased seismic amplitudes, suggesting that there is a threshold value from which seismic data are no longer proportional to the size of the drops. Thunderstorms can be identified by the recording of the sonic waves generated by thunders, with. Single thunders detected to distances of a few tens of kilometers. As the propagation of these acoustic waves is expected to be strongly affected by parameters as air humidity, temperature variations or wind, the seismic data could provide an excellent tool to investigate atmospheric properties variations during thunderstorms

Decoupling between precipitation processes and mountain wave induced circulations observed with a vertically pointing K-Band Doppler radar

Recent studies reported that precipitation and mountain waves induced low tropospheric level circulations may be decoupled or masked by greater spatial scale variability despite generally there is a connection between microphysical processes of precipitation and mountain driven air flows. In this paper we analyse two periods of a winter storm in the Eastern Pyrenees mountain range (NE Spain) with different mountain wave induced circulations and low-level turbulence as revealed by Micro Rain Radar (MRR), microwave radiometer and Parsivel disdrometer data during the Cerdanya-2017 field campaign. We find that during the event studied mountain wave wind circulations and low-level turbulence do not affect neither the snow crystal riming or aggregation along the vertical column nor the surface particle size distribution of the snow. This study illustrates that precipitation profiles and mountain induced circulations may be decoupled which can be very relevant for either ground-based or spaceborne remote sensing of precipitation

Pollutant Concentration Changes During the COVID-19 Lockdown in Barcelona and Surrounding Regions: Modification of Diurnal Cycles and Limited Role of Meteorological Conditions

One of the consequences of the COVID-19 lockdowns has been the modification of the air quality in many cities around the world. This study focuses on the variations in pollutant concentrations and how important meteorological conditions were for those variations in Barcelona and the surrounding area during the 2020 lockdown. Boundary-layer height, wind speed, and precipitation were compared between mid-March and April 2016-2019 (prelockdown) and the same period in 2020 (during lockdown). The results show the limited influence of meteorological factors on horizontal and vertical dispersion conditions. Compared with the pre-lockdown period, during lockdown the boundary-layer height slightly increased by between 5% and 9%, mean wind speed was very similar, and the fraction of days with rainfall increased only marginally, from 0.33 to 0.34, even though April 2020 was extremely wet in the study area. Variations in nitrogen dioxide (NO2), particulate matter with a diameter less than 10 μm (PM10), and ozone (O3) concentrations over a 10-year period showed a 66% reduction in NO2, 37% reduction in PM10, and 27% increase in O3 at a traffi

Precipitation Type Classification of Micro Rain Radar Data Using an Improved Doppler Spectral Processing Methodology

This paper describes a methodology for processing spectral raw data from Micro Rain Radar (MRR), a K-band vertically pointing Doppler radar designed to observe precipitation profiles. The objective is to provide a set of radar integral parameters and derived variables, including a precipitation type classification. The methodology first includes an improved noise level determination, peak signal detection and Doppler dealiasing, allowing us to consider the upward movements of precipitation particles. A second step computes for each of the height bin radar moments, such as equivalent reflectivity (Ze), average Doppler vertical speed (W), spectral width (σ), the skewness and kurtosis. A third step performs a precipitation type classification for each bin height, considering snow, drizzle, rain, hail, and mixed (rain and snow or graupel). For liquid precipitation types, additional variables are computed, such as liquid water content (LWC), rain rate (RR), or gamma distribution parameters, such as the liquid water content normalized intercept (Nw) or the mean mass-weighted raindrop diameter (Dm) to classify stratiform or convective rainfall regimes. The methodology is applied to data recorded at the Eastern Pyrenees mountains (NE Spain), first with a detailed case study where results are compared with different instruments and, finally, with a 32-day analysis where the hydrometeor classification is compared with co-located Parsivel disdrometer precipitation-type present weather observations. The hydrometeor classification is evaluated with contingency table scores, including Probability of Detection (POD), False Alarm Rate (FAR), and Odds Ratio Skill Score (ORSS). The results indicate a very good capacity of Method3 to distinguish rainfall and snow (PODs equal or greater than 0.97), satisfactory results for mixed and drizzle (PODs of 0.79 and 0.69) and acceptable for a reduced number of hail cases (0.55), with relatively low rate of false alarms and good skill compared to random chance in all cases (FAR 0.70). The methodology is available as a Python language program called RaProM at the public github repository

Land Use and Topography Influence in a Complex Terrain Area: A High Resolution Mesoscale Modelling Study over the Eastern Pyrenees using the WRF Model

Different types of land use (LU) have different physical properties which can change local energy balance and hence vertical fluxes of moisture, heat and momentum. This in turn leads to changes in near-surface temperature and moisture fields. Simulating atmospheric flow over complex terrain requires accurate local-scale energy balance and therefore model grid spacing must be sufficient to represent both topography and land-use. In this study we use both the Corine Land Cover (CLC) and United States Geological Survey (USGS) land use databases for use with the Weather Research and Forecasting (WRF) model and evaluate the importance of both land-use classification and horizontal resolution in contributing to successful modelling of surface temperatures and humidities observed from a network of 39 sensors over a 9 day period in summer 2013. We examine case studies of the effects of thermal inertia and soil moisture availability at individual locations. The scale at which the LU classification is observed influences the success of the model in reproducing observed patterns of temperature and moisture. Statistical validation of model output demonstrates model sensitivity to both the choice of LU database used and the horizontal resolution. In general, results show that on average, by a) using CLC instead of USGS and/or b) increasing horizontal resolution, model performance is improved. We also show that the sensitivity to these changes in the model performance shows a daily cycle