Radiosonde Profiles Research Articles

Rare winter‐thunderstorms with hail overnight 12/13 December 2019 over Delhi and its vicinity: an observational study

AbstractA rare overnight thunderstorm with hail on 12/13 December 2019 over Delhi was driven by a Westerly Disturbance (WD) and analysed with the aid of multi‐platform observations. The Doppler weather radar (DWR) detected high reflectivity values (>50dBZ), indicating hail within towering convective clouds. In‐situ measurements showed intense rainfall (totalling ~134mm), accompanied by strong wind gusts and a rapid pressure drop. Satellite data revealed widespread cold cloud tops (<−50°C) associated with the deep convection, while radiosonde profiles indicated atmospheric instability and veering winds during its passage. Lightning observations confirmed energetic convective updrafts. The event highlights the impact of WDs in triggering severe and rare winter convective storms over northern India.

A segmented grid model for vertical adjustment of precipitable water vapor in China

Precipitable Water Vapor (PWV) is vital in climate research, monitoring in troposphere stability and weather disasters. This study investigates the detailed vertical variation of PWV in China, utilizing data from the 5th generation European Centre for Medium-Range Weather Forecasts (ECMWF). A PWV segmented vertical adjustment grid model (CPWV-H) was developed based on a sliding window algorithm, with a spatial resolution of 0.5° × 0.5° and a temporal resolution of one day. The performance of the CPWV-H model was evaluated using multi-source PWV data and compared with the conventional empirical model (EPWV-H) and the recently released C-PWVC1 model. Results show that the CPWV-H model has superior accuracy and stability. Using ERA5 PWV profiles as a reference, the CPWV-H model has a mean Bias of −0.45 mm and a mean RMSE of 1.24 mm, improving by 21.52 % over the EPWV-H model and 31.11 % over the C-PWVC1 model. With radiosonde profiles as a reference, it has a mean Bias of −0.10 mm and a mean RMSE of 2.86 mm, improving by 7.14 % and 13.86 % over the EPWV-H and C-PWVC1 models, respectively. Consequently, the CPWV-H model exhibits enhanced stability and vertical adjustment accuracy in China, offering significant utility for the fusion of multi-source water vapor data, comparative analysis, and climate research.

Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models

Abstract. Accurate and continuous estimates of the thermodynamic structure of the lower atmosphere are highly beneficial to meteorological process understanding and its applications, such as weather forecasting. In this study, the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval is used to retrieve temperature and humidity profiles from various combinations of input data collected by passive and active remote sensing instruments, in situ surface platforms, and numerical weather prediction models. Among the employed instruments are microwave radiometers (MWRs), infrared spectrometers (IRSs), radio acoustic sounding systems (RASSs), ceilometers, and surface sensors. TROPoe uses brightness temperatures and/or radiances from MWRs and IRSs, as well as other observational inputs (virtual temperature from the RASS, cloud-base height from the ceilometer, pressure, temperature, and humidity from the surface sensors) in a physical iterative retrieval approach. This starts from a climatologically reasonable profile of temperature and water vapor, with the radiative transfer model iteratively adjusting the assumed temperature and humidity profiles until the derived brightness temperatures and radiances match those observed by the MWR and/or IRS instruments within a specified uncertainty, as well as within the uncertainties of the other observations, if used as input. In this study, due to the uniqueness of the dataset that includes all the abovementioned sensors, TROPoe is tested with different observational input combinations, some of which also include information higher than 4 km above ground level (a.g.l.) from the operational Rapid Refresh numerical weather prediction model. These temperature and humidity retrievals are assessed against independent collocated radiosonde profiles under non-cloudy conditions to assess the sensitivity of the TROPoe retrievals to different input combinations.

Implementation and tests of the NLT 3D scheme in the ICON model

AbstractThe main goal of this article is to test the long‐term performance of the three‐dimensional non‐local turbulence (NLT) parameterization scheme at different grid sizes in the so‐called gray zone between classical mesoscale modeling ( several km) and large eddy simulations (LES: several 100 m). For this, NLT has been implemented in the numerical weather prediction Icosahedral Nonhydrostatic model (ICON) of Deutscher Wetterdienst (DWD). Results are compared with a one‐dimensional version of NLT (NLT) and with two operational turbulence schemes available in ICON. Comparisons with observations from radiosondes, the operational surface synoptic (SYNOP) station network, and RAdar‐OnLine‐ANeichung (RADOLAN) radar data of DWD indicate that all turbulence schemes investigated perform reasonably well. Nonetheless, a more detailed study of the model results reveals several interesting differences between the turbulence parameterizations to be discussed in detail. Median absolute errors (MAE) from point‐to‐point comparisons between numerical results and SYNOP observations tend to be smaller than those from comparisons with averaging simulated fields over an environment around each station location. This behavior indicates an information loss caused by the averaging process. For the 2‐m temperature () and the hourly precipitation sums (), MAEs decrease with decreasing grid sizes, thus suggesting an information gain for finer grids. The nighttime MAEs of and obtained with NLT and NLT are similar to or lower than those of the operational turbulence schemes of ICON. Moreover, during a shallow warm‐air intrusion, NLT and especially NLT yield a more realistic representation of the horizontal structures of and, during nighttime stable boundary‐layer situations, also . Radiosonde profiles of the potential temperature confirm a reasonable vertical mixing as obtained with NLT and NLT.

A new Egyptian Grid Weighted Mean Temperature (EGWMT) model using hourly ERA5 reanalysis data in GNSS PWV retrieval

Precise modeling of weighted mean temperature (Tm) is essential for Global Navigation Satellite System (GNSS) meteorology. In retrieving precipitable water vapor (PWV) from GNSS, Tm is a crucial parameter for the conversion of zenith wet delay (ZWD) into PWV. In this study, an improved Tm model, named EGWMT, was developed to accurately estimate Tm at any site in Egypt. This new model was established using hourly ERA5 reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF) covering the period from 2008 to 2019 with a spatial resolution of 0.25° × 0.25°. The performance of the proposed model was evaluated using two types of data sources, including hourly ERA5 reanalysis data from 2019 to 2022 and radiosonde profiles over a six-year period from 2017 to 2022. The accuracy of the EGWMT model was compared to that of four other models: Bevis, Elhaty, ANN and GGTm-Ts using two statistical quantities, including mean absolute bias (MAB) and root mean square error (RMSE). The results demonstrated that the EGWMT model outperformed the Bevis, Elhaty, ANN and GGTm-Ts models with RMSE improvements of 32.5%, 30.8%, 39% and 48.2%, respectively in the ERA5 data comparison. In comparison with radiosonde data, the EGWMT model achieved RMSE improvements of 22.5%, 34%, 38% and 19.5% against Bevis, Elhaty, ANN and GGTm-Ts models, respectively. In order to determine the significance of differences in means and variances, statistical tests, including t-test and F-test, were conducted. The results confirmed that there were significant differences between the EGWMT model and the four other models.

Examining the Impact of Assimilating Surface, PBL, and Free Atmosphere Observations from TORUS on Analyses and Forecasts of Two Supercells on 8 June 2019

Abstract This study describes data-denial experiments conducted to examine the impact of assimilating subsets of data from the TORUS project on storm-scale ensemble forecasts of two supercells on 8 June 2019. Assimilated data from TORUS includes mobile mesonet, UAS, and radiosonde observations. The TORUS data are divided into three spatial subsets to evaluate the importance of observing different parts of the atmosphere on forecasts of this case: the SFC subset consisting of just the near-surface mobile mesonet observations, the PBL subset consisting of UAS observations and radiosonde profiles below 762 m, and the FREE subset consisting of radiosonde profiles above 762 m. Data denial experiments are then conducted by comparing analyses and free forecasts generated using a cycled EnKF data assimilation system assimilating conventional observations, radar observations, and all of the TORUS observations at once with experiments where one of the three subsets is removed in turn as well as a control experiment assimilating only conventional and radar observations. Our results show that assimilating all of the TORUS observations at once in the ALL experiment improves the storm-scale ensemble forecasts much more often than it degrades them, and that no one subset of the TORUS data was consistently most important for improving the analyses or forecasts.

Exploring the daytime boundary layer evolution based on Doppler spectrum width from multiple coplanar wind lidars during CROSSINN

Abstract. Over heterogeneous, mountainous terrain, the determination of spatial heterogeneity of any type of a turbulent layer has been known to pose substantial challenges in mountain meteorology. In addition to the combined effect in which buoyancy and shear contribute to the turbulence intensity of such layers, it is well known that mountains add an additional degree of complexity via non-local transport mechanisms, compared to flatter topography. It is therefore the aim of this study to determine the vertical depths of both daytime convectively and shear-driven boundary layers within a fairly wide and deep Alpine valley during summertime. Specifically, three Doppler lidars deployed during the CROSSINN (Cross-valley flow in the Inn Valley investigated by dual-Doppler lidar measurements) campaign within a single week in August 2019 are used to this end, as they were deployed along a transect nearly perpendicular to the along-valley axis. To achieve this, a bottom-up exceedance threshold method based on turbulent Doppler spectrum width sampled by the three lidars has been developed and validated against a more traditional bulk Richardson number approach applied to radiosonde profiles obtained above the valley floor. The method was found to adequately capture the depths of convective turbulent boundary layers at a 1 min temporal and 50 m spatial resolution across the valley, with the degree of ambiguity increasing once surface convection decayed and upvalley flows gained in intensity over the course of the afternoon and evening hours. Analysis of four intensive observation period (IOP) events elucidated three regimes of the daytime mountain boundary layer in this section of the Inn Valley. Each of the three regimes has been analysed as a function of surface sensible heat flux H, upper-level valley stability Γ, and upper-level subsidence wL estimated with the coplanar retrieval method. Finally, the positioning of the three Doppler lidars in a cross-valley configuration enabled one of the most highly spatially and temporally resolved observational convective boundary layer depth data sets during daytime and over complex terrain to date.

Thermodynamic and stability features of the atmospheric boundary layer over Kochi during different monsoon seasons based on radiosonde profiles

The characteristics of thermodynamic parameters at different pressure levels have been studied using radiosonde data for the period 1989–2019 during different seasons incorporating sea-land breeze circulation influences over Kochi, in western peninsular India. It is also observed that the variation in lower tropospheric stability of (LTS) is found to be influenced by other thermodynamic parameters, such as temperature, mixing ratio, potential temperature, equivalent potential temperature, CAPE, and CINE at various pressure levels. The average value of temperature, potential temperature, mixing ratio, and equivalent potential temperature was maximum during the pre-monsoon season. At 700 hPa level, the average value of the mixing ratio was high during the sea breeze circulation of pre-monsoon season. The average value of equivalent potential temperature at 700 hPa level was maximum for the southwest monsoon season. A decreasing trend was observed in lower tropospheric stability (LTS) over the region in all seasons except for the winter season during the sea breeze circulation. The positive trend of LTS was observed during the sea breeze circulation time during the winter season, and it has a strong correlation with the positive trend in temperature and potential temperature at the 700 hPa level. Moreover, the southwest monsoon season exhibited a substantial decrease in LTS, potentially associated with an increasing trend in temperature, potential temperature, mixing ratio, and equivalent potential temperature at the surface level. CAPE has a significant increasing trend only for the sea breeze circulation period during the monsoon season. Additionally, CINE displayed an increasing trend across all seasons, with higher values observed during the winter and post-monsoon seasons.

A climatology of Arctic fog along the coast of East Greenland

AbstractThis study presents a comprehensive climatology of coastal fog from four synoptic weather stations operated by the Danish Meteorological Institute along the entire East Greenland coast between 1958 and 2016. Elements investigated include fog frequency, daily timing, temperature, wind, visibility and radiosonde profiles during fog. The spatiotemporal patterns in fog from the low‐ to high‐Arctic locations were related to varying regional seasonal temperatures, surface and upper‐air wind and sea ice conditions, and to correlations with the North Atlantic Oscillation (NAO) and the Greenland Blocking Index (GBI). Results indicate that ˜70–80% of East Greenland fog occurs in summer (MJJA), and yearly fog onset is near‐coincident with the start of sea ice break‐up. This warm‐season fog has the typical characteristics of advection fog, as shown in the radiosonde profiles and the association with a gentle sea breeze. More than 95% of warm‐season fog is warmer than −10°C, and peaks close to 0°C and, therefore, consists of liquid or supercooled water droplets. In the cold season, mixed‐phase fog prevails in the high‐Arctic locations, accounting for ˜70% of observations. Ice fog (T < −30°C) occurs in only 2% of observations and is limited to Northeast Greenland during the cold season. The cold‐season composite radiosonde fog profiles in the high‐Arctic locations are characterized by deep (˜1000 m) and strong (˜6°C) surface‐based temperature inversions. Visibility during most fog conditions is lowest during the warm season (<500 m) and highest during the cold season (<800 m). In Northeast Greenland, visibility during warm‐season fog has decreased by ˜50 m·dec−1 between 1981 and 2016. In Southeast Greenland, fog visibility is high during low GBI and a positive phase of NAO, but no other correlations with climate indices were found.

Increasing Antarctic Ice Mass to Help Offset Sea Level Rise

Global sea level is predicted to rise for centuries even if greenhouse gas emissions are greatly reduced. Sea level rise (SLR) threatens coastal communities where a large fraction of the human population lives. A possible mitigation effort is to increase the ice mass in Antarctica. Coastal Antarctic radiosonde profiles are supersaturated with respect to ice on average 47% of the time. If all of this excess water vapor and supercooled liquid cloud water were removed from the atmosphere and deposited on the Antarctic landmass, it would offset 11 cm of SLR by 2100, or about 15 (8–17) percent of the predicted SLR. This strategy could be used to supplement other efforts to reduce climate change impacts, such as carbon dioxide removal or solar climate intervention.

The diurnal variation of wet bulb temperatures and exceedance of physiological thresholds relevant to human health in South Asia

Extreme heat is increasingly being acknowledged as a serious hazard to human health, through a combination of physiological responses to heat, expressed as dry and wet bulb temperatures, and personal factors. Here we present an analysis of the diurnal variability of dry and wet bulb temperatures using station data in South Asia during both regular and heatwave days. We find that diurnal cycles differ, with the daily maximum wet bulb temperature occurring several hours after the daily maximum dry bulb temperature. Using radiosonde profiles, we show that the timing and amplitude of the diurnal variability of wet bulb temperature can be explained by changes in boundary layer depths and water content. Physiological thresholds for uncompensable heat stress were exceeded even in the evenings, many hours after dry bulb temperature peaks. Cumulative exceedances occurred in 105 instances, corresponding to at least 300 hours of exposure to uncompensable heat stress in South Asia between 1995 and 2020. We conclude that physiologically relevant thresholds provide a more robust way to estimate health impacts, and that wet bulb temperature alone is insufficient as an indicator of hazardous heat.

Evaluating Atmospheric Surface Layer Flux Parameterization within the Coastal Regime

Abstract Traditional atmospheric surface layer theory assumes homogeneous surface conditions. Regardless, nearly all surface layer parameterization schemes employed within numerical weather prediction models utilize the same techniques within highly heterogeneous coastal regimes as for homogeneous environments. We compare predicted surface weather and fluxes of momentum, heat, and moisture—focusing mainly on momentum—from regional simulations using the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) atmospheric model to observations collected from offshore buoys, inland flux towers, and radiosonde profiles during the Coastal Land-Air-Sea Interaction (CLASI) project throughout the summer of 2021 around Monterey Bay, California. Results reveal that modeled cross-coastal surface flux gradients are spuriously discontinuous, leading to systematically overestimated fluxes and weak winds inland of the coastline during onshore flow periods. Additionally, contrary to observations, modeled surface exchange coefficients are insensitive to wind direction on both sides of the coast, which degrades predictive skill downstream from the coastline. Over the central bay, prediction degrades when near-surface wind directions deviate from the prevailing flow direction as the parameterized stress–wind relationship fails during these cases. Predictive skill over the bay is therefore linked to variations in wind direction. Offshore of the geographically complex peninsula, systematic biases are less clear; however, bifurcations in drag coefficients based on wind direction were measured here as well. Last, increasing the horizontal grid spacing from 333 m to 3 km does not significantly affect surface layer prediction. This work highlights the need to reevaluate surface layer parameterization methods for modeling within coastal regions. Significance Statement Understanding surface layer weather is critical for many purposes, such as infrastructure design and weather forecasting. Within the context of numerical modeling and weather prediction, skillful forecasts of surface winds and temperature rely on accurate portrayal of the surface layer. By comparing observations collected during the Coastal Land-Air-Sea Interaction field program to numerical model solutions, we show that prediction of the surface layer fluxes of momentum, heat, and moisture break down near the coastline, which leads to biases in the predicted surface layer weather both inland and over the water. As surface layer parameterization methods across nearly all numerical models are rooted in the same practices, our results call into question the use of traditional methods near the coastline.

Near-Cloud Atmospheric Ingredients for Deep Convection Initiation

Abstract A lack of routine environmental observations located near deepening cumulus congestus clouds limits verification of important theorized and simulated updraft–environment interaction processes occurring during deep convection initiation (CI). We analyze radiosonde profiles collected during several hundred CI events near a mountain range in central Argentina during the CACTI field campaign. Statistical analyses illustrate environmental conditions supporting radar-observed CI outcomes that span a spectrum of convective cell depths, widths, and durations, as well as events lacking precipitating convection. Tested environmental factors include a large variety of sounding-derived measurements of CAPE, CIN, moisture, terrain-relative winds, vertical shear, and lifted parcel properties, with supplemental model reanalysis of background larger-scale vertical motion. CAPE and CIN metrics do not consistently differentiate CI success from failure. Only a few environmental factors contain consistent monotonic relationships among the spectrum of cloud depths achieved during CI: (i) the depth and strength of background ascent, and (ii) the component of low-level flow oriented parallel to the ridgeline. These metrics suggest that the ability of the surrounding flow to lift parcels to their LFC and terrain-modified flow are consistently relevant processes for CI. Low- to midlevel relative humidity strongly discriminated between CI and non-CI events, likely reflecting entrainment-driven dilution processes. However, we could not confidently conclude that relative humidity similarly discriminated robust from marginal CI events. Circumstantial evidence was found linking cell width, an important cloud property governing the probability of CI, to LCL height, boundary layer depth, depth and magnitude of the CIN layer, and ambient wind shear.

Use of multiplatform in-situ observation to study vertical structure and microphysics of clouds during southwest monsoon over Western Ghats, India

Combination of radiosonde profiles with collocated in-situ ground and aircraft measurements is used for the first time to study the vertical structure and microphysics of clouds during southwest monsoon over the Western Ghats, India. The morphology of clouds is detailed with the help of radiosonde observations and classified as low, mid, and high-level clouds depending on the cloud base height. Radiosonde sounding profiles indicated occurrences of both single and multi-layered clouds with higher occurrences of single-layered (∼35%) clouds during monsoon transition period (June and September) and two-layered (∼42%) during core monsoon period (July and August). Dominance of low (∼30%) and high-level (∼60%) clouds were noticed compared to mid-level clouds over the observational site during the southwest monsoon.Warm cloud microphysics was investigated using collocated ground and airborne in situ measurements. Irrespective of the cloud type, the cloud liquid water content and the effective droplet diameter increased with altitude. One of the key results is the rapid broadening of the cloud droplet size distribution with height. The number concentration of droplets above 25 μm diameters showed a steep decrease at altitudes above 1800 m, suggesting active collision-coalescence.

Assimilating Sentinel‐3 All‐Sky PWV Retrievals to Improve the WRF Forecasting Performance Over the South China

AbstractWater vapor is a key driver for the evolution of weather system. To investigate the impact of assimilating Sentinel‐3 precipitable water vapor (PWV) on weather forecasting, Sentinel‐3 PWV retrievals over the South China with two different assimilation schemes are assimilated into the Weather Research and Forecasting (WRF) model. In the first assimilation scheme, only Sentinel‐3 clear‐sky PWV are assimilated, while Sentinel‐3 all‐sky PWV are assimilated for the second assimilation scheme. For both data assimilation schemes, we totally conduct 28 WRF data assimilation runs and forecasts for 28 selected days over two periods, that is, 14 days in March 2020 and 14 days in June 2020. The weather condition in June 2020 is much wetter than March 2020. Generally, assimilating Sentinel‐3 PWV improves the WRF forecasting performance, particularly for June 2020. Assimilation of all‐sky PWV outperforms assimilation of clear‐sky PWV. The comparison results with radiosonde profiles show that assimilating Sentinel‐3 PWV appreciably corrects the bias of WRF water vapor mixing ratio forecasting results for June 2020. The rainfall validation results show that both assimilation schemes show a positive impact in June 2020, but a neutral impact in March 2020. For June 2020, assimilating Sentinel‐3 all‐sky PWV improves rainfall forecast skill score by 2.4%, while the rainfall forecast score is improved by 1.0% after assimilating clear‐sky PWV. Additionally, assimilation of Sentinel‐3 PWV can modify the WRF moisture field, which further improves the rainfall spatial pattern.

Evaluating wind profiles in a numerical weather prediction model with Doppler lidar

Abstract. We use Doppler lidar wind profiles from six locations around the globe to evaluate the wind profile forecasts in the boundary layer generated by the operational global Integrated Forecast System (IFS) from the European Centre for Medium-range Weather Forecasts (ECMWF). The six locations selected cover a variety of surfaces with different characteristics (rural, marine, mountainous urban, and coastal urban). We first validated the Doppler lidar observations at four locations by comparison with co-located radiosonde profiles to ensure that the Doppler lidar observations were of sufficient quality. The two observation types agree well, with the mean absolute error (MAE) in wind speed almost always less than 1 m s−1. Large deviations in the wind direction were usually only seen for low wind speeds and are due to the wind direction uncertainty increasing rapidly as the wind speed tends to zero. Time–height composites of the wind evaluation with 1 h resolution were generated, and evaluation of the model winds showed that the IFS model performs best over marine and coastal locations, where the mean absolute wind vector error was usually less than 3 m s−1 at all heights within the boundary layer. Larger errors were seen in locations where the surface was more complex, especially in the wind direction. For example, in Granada, which is near a high mountain range, the IFS model failed to capture a commonly occurring mountain breeze, which is highly dependent on the sub-grid-size terrain features that are not resolved by the model. The uncertainty in the wind forecasts increased with forecast lead time, but no increase in the bias was seen. At one location, we conditionally performed the wind evaluation based on the presence or absence of a low-level jet diagnosed from the Doppler lidar observations. The model was able to reproduce the presence of the low-level jet, but the wind speed maximum was about 2 m s−1 lower than observed. This is attributed to the effective vertical resolution of the model being too coarse to create the strong gradients in wind speed observed. Our results show that Doppler lidar is a suitable instrument for evaluating the boundary layer wind profiles in atmospheric models.

A Multi-Model Ensemble Pattern Method to Estimate the Refractive Index Structure Parameter Profile and Integrated Astronomical Parameters in the Atmosphere

In this study, we devised a constraint method, called multi-model ensemble pattern (MEP), to estimate the refractive index structure parameter (Cn2) profiles based on observational data and multiple existing models. We verified this approach against radiosonde data from field campaigns in China’s eastern and northern coastal areas. Multi-dimensional statistical evaluations for the Cn2 profiles and integrated astronomical parameters have proved MEP’s relatively reliable performance in estimating optical turbulence in the atmosphere. The correlation coefficients of MEP and measurement overall Cn2 in two areas are up to 0.65 and 0.76. A much higher correlation can be found for a single radiosonde profile. Meanwhile, the difference evaluation of integrated astronomical parameters also shows its relatively robust performance compared to a single model. The prowess of this reliable approach allows us to carry out regional investigation on optical turbulence features with routine meteorological data soon.

Region-Specific and Weather-Dependent Characteristics of the Relation between GNSS-Weighted Mean Temperature and Surface Temperature over China

Weighted mean temperature of the atmosphere, Tm, is a key parameter for retrieving the precipitable water vapor from Global Navigation Satellite System observations. It is commonly estimated by a linear model that relates to surface temperature Ts. However, the linear relationship between Tm and Ts is associated with geographic regions and affected by the weather. To better estimate the Tm over China, we analyzed the region-specific and weather-dependent characteristics of this linear relationship using 860,054 radiosonde profiles from 88 Chinese stations between 2005 and 2018. The slope coefficients of site-specific linear models are 0.35~0.95, which generally reduce from northeast to southwest. Over southwest China, the slope coefficient changes drastically, while over the northwest, it shows little variation. We developed a Ts∼Tm linear model using the data from rainless days as well as a model using the data from rainy days for each station. At half the stations, mostly located in west and north China, the differences between the rainy-day and rainless-day Tm models are significant and larger than 0.5% (1%) in mean (maximal) relative bias. The regression precisions of the rainy-day models are higher than that of the rainless-day models averagely by 28% for the stations. Radiosonde data satisfying Tm−Ts>10 K and Ts−Tm>30 K most deviate from linear regression models. Results suggest that the former situation is related to low surface temperature (<270 K), as well as striking temperature and humidity inversions below 800 hPa, while the latter situation is related to high surface temperature (>280 K) and a distinct humidity inversion above 600 hPa.

Nowcasting of Wind in the Venice Lagoon Using WRF-FDDA

The Four-Dimensional Data Assimilation module (FDDA) is used in combination with the WRF model for the analysis of two case studies of high tide (on 4 April 2019 and on 12 November 2019) that affected the Venice Lagoon in the recent past. The system is implemented in the perspective of an operational use for nowcasting of 10 m wind, which will be part of a numerical system aimed at the forecast of the sea level height in the Venice Lagoon. The procedure involves the assimilation of data from meteorological surface stations distributed within the Venice Lagoon and in the open northern Adriatic Sea in front of the lagoon, as well asthe radiosonde profiles available within the simulation domain. The two cases were selected considering that the real-time forecasts missed their evolution, and the sea level height was significantly underpredicted. The comparison of the simulated wind with the observations shows a fairly good agreement over short time scales (1–2 h) in both cases; hence, the WRF-FDDA system represents a promising tool and a possibly valuable support to the decision makers in case of high tide in the Venice Lagoon.

Assessment of AIRS Version 7 Temperature Profiles and Low-Level Inversions with GRUAN Radiosonde Observations in the Arctic

The extensive global climate observing system (GCOS) reference upper-air network (GRUAN) datasets provide a chance to validate newly released Atmospheric Infrared Sounder (AIRS) version 7 (v7) products over the Arctic. This manuscript reports on the analysis performed to evaluate errors from AIRS version 6 (v6) and v7 temperature profiles and to characterize the derived low-level temperature inversion (LLI) representativeness in the Arctic region. The AIRS averaging kernel, representing the AIRS measurement sensitivity, is applied to reduce the vertical resolution of the radiosonde profiles for comparison. Due to improved retrieval algorithms, v7 produces smaller biases in the troposphere and suppresses the cold bias in v6. Nevertheless, the profile-averaged root mean square error (RMSE) increased by over 30% in v7, particularly in the winter half-year when v7 showed a larger RMSE below 800 hPa. The AIRS temperature retrieval accuracy is primarily sensitive to surface type and cloud fraction. Compared to v6, v7 has less bias over frozen land and sea ice in different cloud fraction conditions. However, the RMSEs of v7 are more sensitive to the effective cloud fraction (ECF) and are highly influenced by a more significant contribution from nonfrozen land samples. Compared to the kernel-averaged radiosonde profiles, more than 80% of the temperature profiles from v6 and v7 accurately detect LLIs. The discreteness of the AIRS’s predefined pressure level results is consistent with the radiosondes only 65% of the time for LLI depth calculation. In contrast, the AIRS can obtain LLI intensity with a relatively high correlation (>0.9). With the AIRS temperature retrieval in the boundary layer further improved, it has the potential to be used as an independent LLI detector in the Arctic region.