Comment on egusphere-2022-1488

A better carbon-water flux simulation in multiple vegetation types by data assimilation

This article illustrates how multifrequency radar observations can refine the mass–size parameterization of frozen hydrometeors in scattering models and improve the correlation between the radar observations and in situ measurements of microphysical properties of ice and snow. The data presented in this article were collected during the GPM Cold Season Precipitation Experiment (GCPEx) (2012) and Olympic Mountain Experiment (OLYMPEx) (2015) field campaigns, where the true mass–size relationship was not measured. Starting from size and shape distributions of ice particles measured in situ, scattering models are used to simulate an ensemble of reflectivity factors for various assumed mass–size parameterizations (MSP) of the power-law type. This ensemble is then collocated to airborne and ground-based radar observations, and the MSPs are refined by retaining only those that reproduce the radar observations to a prescribed level of accuracy. A versatile “retrieval dashboard” is built to jointly analyze the optimal MSPs and associated retrievals. The analysis shows that the optimality of an MSP depends on the physical assumptions made in the scattering simulators. This work confirms also the existence of a relationship between parameters of the optimal MSPs. Through the MSP optimization, the retrievals of ice water contentMand mean diameterDmseem robust to the change in meteorological regime (between GCPEx and OLYMPEx); whereas the retrieval of the diameter spreadSmseems more campaign dependent.

Abstract. Accurate estimation of the melting level (ML) is essential in radar rainfall estimation to mitigate the bright band enhancement, classify hydrometeors, correct for rain attenuation and calibrate radar measurements. This paper presents a novel and robust ML-detection algorithm based on either vertical profiles (VPs) or quasi-vertical profiles (QVPs) built from operational polarimetric weather radar scans. The algorithm depends only on data collected by the radar itself, and it is based on the combination of several polarimetric radar measurements to generate an enhanced profile with strong gradients related to the melting layer. The algorithm is applied to 1 year of rainfall events that occurred over southeast England, and the results were validated using radiosonde data. After evaluating all possible combinations of polarimetric radar measurements, the algorithm achieves the best ML detection when combining VPs of ZH, ρHV and the gradient of the velocity (gradV), whereas, for QVPs, combining profiles of ZH, ρHV and ZDR produces the best results, regardless of the type of rain event. The root mean square error in the ML detection compared to radiosonde data is ∼200 m when using VPs and ∼250 m when using QVPs.

<div class="section abstract"><div class="htmlview paragraph">Cloud phase discrimination, coupled with measurements of liquid water content (LWC) and ice water content (IWC) as well as the detection and discrimination of supercooled large droplets (SLD), are of primary importance in aviation safety due to several high-profile incidents over the past two decades. The UTC Aerospace Systems Optical Ice Detector (OID) is a prototype laser sensor intended to discriminate cloud phase, to quantify LWC and IWC, and to detect SLD and differentiate SLD conditions from those of Appendix C. Phase discrimination is achieved through depolarization scattering measurements of a circularly polarized laser beam transmitted into the cloud. Optical extinction measurements indicate the liquid and ice water contents, while the differential backscatter from two distinct probe laser wavelengths implies an effective droplet size.</div><div class="htmlview paragraph">The OID is designed to be flush-mounted with the aircraft skin and to sample the air stream beyond the boundary layer of the aircraft. The volume of the sampled airstream is several orders of magnitude greater than that of traditional airborne cloud probes, such as the Forward Scattering Spectrometer Probe (FSSP). The OID has flown on the University of North Dakota (UND) Cessna Citation-II research aircraft in a wide variety of atmospheric conditions, which cloud probes on the aircraft have verified to corroborate the OID data. In this paper, we compare UND cloud probe measurements with those from the UTC Aerospace Systems OID to demonstrate the possibility of using the OID to meet the need for measurements of various icing parameters.</div></div>

Ice water content is estimated using polarimetric radar measurements at 3 GHz. The measurements were taken during a light stratiform precipitation event. Vertical profiles of reflectivity (Z/sub H/), differential reflectivity (Z/sub DR/) and specific differential phase (K/sub DP/) show clear bright band in all three cases. Using a melting layer model, the average mixture density of the particles just before melting is estimated. Then, using Z/sub DR/ and K/sub DP/, an attempt is made to derive the ice water content at various heights above the melting layer. The values so derived seem to agree qualitatively with reflectivity based estimates, although the latter suffers from large uncertainties because of variations in bulk density and size distribution. >

Dual-Doppler techniques for 3D wind retrievals have been widely used for some time, but very little effort has been dedicated to their validation. In particular, the vertical component of the wind field w is not well sampled by the radars due to the scanning strategies used for weather radars, and relies heavily on imposed conditions such as the mass continuity equation and the vorticity equation. Therefore, validation of the retrieved w with independent observations remains important before using the retrievals for the study of weather systems dynamics. The polarimetric Doppler radars JuXPol and BoXPol perform volume scans followed by a Range Height Indicator (RHI) scan and a vertically pointing scan. In this study we use the latter to derive w above the BoXPol radar and to compare it to the vertical component of the wind field retrieved by the Dual-Doppler technique. Both profiles of w show similar overall shapes with minima and maxima situated at about the same heights. The values of w obtained from the vertical scan are within the range of the profiles obtained with the Dual-Doppler method, except above an altitude of 4km where the vertical scan shows higher upward velocities than the Dual-Doppler. This can be caused by a tilted updraft which can only be partly sampled by the vertical scan, and results from different sampling times. Bias and root mean square error indicate a relative good agreement between both estimates especially for the median and high values of w, but an underestimation of the Dual-Doppler w at the lowest values.

In this study, the accuracy of ocean analysis data, which are produced from the Korea Meteorological Administration (KMA) Nucleus for European Modelling of the Ocean/Variational Data Assimilation (NEMO/NEMOVAR, hereafter NEMO) system and the HYbrid Coordinate Ocean Model/Navy Coupled Ocean Data Assimilation (HYCOM/NCODA, hereafter HYCOM) system, was evaluated using various oceanic observation data from March 2015 to February 2016. The evaluation was made for oceanic thermal environments in the tropical Pacific, the western North Pacific, and the Korean peninsula. NEMO generally outperformed HYCOM in the three regions. Particularly, in the tropical Pacific, the RMSEs (Root Mean Square Errors) of NEMO for both the sea surface temperature and vertical water temperature profile were about 50% smaller than those of HYCOM. In the western North Pacific, in which the observational data were not used for data assimilation, the RMSE of NEMO profiles up to 1000 m (0.49℃) was much lower than that of HYCOM (0.73℃). Around the Korean peninsula, the difference in RMSE between the two models was small (NEMO, 0.61oC; HYCOM, 0.72oC), in which their errors show relatively big in the winter and small in the summer. The differences reported here in the accuracy between NEMO and HYCOM for the thermal environments may be attributed to horizontal and vertical resolutions of the models, vertical coordinate and mixing scheme, data quality control system, data used for data assimilation, and atmosphere forcing. The present results can be used as a basic data to evaluate the accuracy of NEMO, before it becomes the operational model of the KMA providing real-time ocean analysis and prediction data.

Force and vibration generated in apical direction by three endodontic files of different kinematics during simulated canal preparation: An in vitro analytical study.

Three-Dimensional Quantitative Comparative Analysis of Trapezial-Metacarpal Joint Surface Curvatures in Human Populations

<p>Mesoscale and sub-mesoscale features such as eddies, fronts, boundary currents and localised upwellings play an important role in ocean dynamics, particularly in the coastal / shelf seas which are characterised by the values of Rossby radius much smaller than in the open ocean. Fine resolution ocean modelling is a ubiquitous practice to resolve such features. Due to inevitable errors,ocean models tend to drift from reality. A process called data assimilation (DA) is a way of keeping a model ‘on the tracks’ by constantly correcting it with fresh observations (DARC, 2021). Due to their complexity, operational data assimilation systems, such as NEMOVAR, DART or PDAF remained a topic mostly reserved to experts (Carrassi et al, 2018). This study presents a simple and computationally efficient method for DA in a high-resolution 3D ocean model, which is nested into a coarse-resolution good quality data assimilating (parent) model. The method is particularly suitable for localised coastal models which are run by small modelling groups. The method named Data Assimilation with Stochastic-Deterministic Downscaling (SDDA) reduces bias and root mean square errors (RMSE) of the high-resolution model. The basic idea is to assimilate data from the parent model instead of actual observations. In this way, the high-resolution model is physically aware of observations via the parent model. The method allows to avoid a complex process of assimilating the same observations which were already assimilated into the parent model. The method consists of two stages: (1) downscaling the parent model output onto the child model grid using Stochastic-Deterministic Downscaling (Shapiro et al, 2021), and (2) applying a Kalman gain formula to each of the fine grid nodes. The method is illustrated in a synthetic 2D case where the true solution is known, and the high-resolution model forecast (before data assimilation) is simulated by adding various types of errors. The SDDA method reduces the child model bias to the same level as in the parent model and reduces the RMSE typically by a factor of 2 to 5.</p><p>References</p><p>DARC, 2021 available at https://research.reading.ac.uk/met-darc/aboutus/what-is-data-assimilation/ last access: 05 August 2021</p><p>Carrassi, A., Bocquety ,M., Bertino, L. and Evensen,G. 2018. Data assimilation in the geosciences: An overview of methods, issues, and perspectives. WIRES climate change, v9, No5.</p><p>Shapiro, G. I., Gonzalez-Ondina, J. M., and Belokopytov, V. N., 2021. High-resolution stochastic downscaling method for ocean forecasting models and its application to the Red Sea dynamics, Ocean Sci., 17, 891–907.</p>

The aim of the present research is to calculate the Relative Accuracy (R.A) and Root Mean Square Error (RMSE) for the location of Technical Institute, Mosul in Nineveh Governorate, Iraq. at the northern part of Mosul city by using surveying Instruments (GPS 1230 , Total Station 06 & Level NAK2), to calculate the coordinates and elevations of (16) Ground Control Points which covered the study area. The field data, remote sensing techniques and GIS software (Arc Map GIS10.3) are used to check the digital map of study area that helped the administrator to take the correct decision, determining positions of the future expansionist for buildings, roads, measuring lengths, areas and there coordinates, directions as well as the total costs estimation needed for any development from the final map. The gradient percentage between the highest and lowest points is calculated to be (9.954%). Relative accuracy for the based measured points (R.A) = 1:60000), and Root Mean Square error in Easting coordinates (RMSE) = ± 0.102m, in Northing coordinates (RMSN) = ± 0.096m, the Root Mean Square for the resultant (RMSR) = ± 0.099m, and Root Mean Square for Elevations ( RMSRL)= ± 0.005m. We Matched and correlated (10) measured points from study area with their graphic data positions on the aerial photo, determining the offsets for position and elevations as follows: (RMSE = ± 0.076m), (RMSN = ± 0.125m), (RMSR = ± 0.088m), and (Root Mean Square for Elevations = RMSRL= ± 0.045m). This research enable us after checking the results to produce new maps from Aerial photograph or Digital Image to use it for land uses and future extensions in whole study area.

Radar antennas in the open air are affected by dynamic wind loading during azimuthal scanning. In this state, the acquisition and prediction of the aerodynamic load is the focus of the structure design for the antenna. We performed wind tunnel force tests on flat plate antennas with different aspect ratios. The changes in mean, maximum, and root mean square values of the aerodynamic coefficients relative to the antenna aspect ratio and reduced frequency are given for the first time. The effects of the antenna aspect ratio and reduced frequency on aerodynamic characteristics are analyzed. The experimental results indicate that the magnitude of the azimuthal moment is related to the antenna aspect ratio and the reduced frequency. It is worth noting that the antenna aspect ratio has a significant impact on the mean, maximum, and root mean square values of dynamic aerodynamic coefficients. The mean, maximum, and root mean square values of the rolling moment coefficient, azimuth moment coefficient, and pitching moment coefficient monotonously decrease with the increase in aspect ratio. In addition, the influence of reduced frequency on the dynamic aerodynamic coefficient is closely related to the antenna aspect ratio. Under the same antenna aspect ratio, increasing the reduced frequency will increase the mean, maximum, and root mean square values of the aerodynamic coefficient. For flat plate antennas with different aspect ratios, at the condition of increasing the aspect ratios, the mean, maximum, and root mean square values of aerodynamic coefficients do not change significantly with the change of reduced frequency.

Improving flood forecasting using conditional bias-aware assimilation of streamflow observations and dynamic assessment of flow-dependent information content

The potential of millimeter-wavelength radar-based ice water content (IWC) estimation is demonstrated using a Ka-band Scanning Polarimetric Radar (KASPR) for the U.S. northeast coast winter storms. Two IWC relations for Ka-band polarimetric radar measurements are proposed: one that uses a combination of the radar reflectivity Z and the estimated total number concentration of snow particles Nt and the other based on the joint use of Z, specific differential phase KDP, and the degree of riming frim. A key element of the algorithms is to obtain the “Rayleigh-equivalent” value of Z measured at the Ka band, i.e., the corresponding Z at a longer radar wavelength for which Rayleigh scattering takes place. This is achieved via polarimetric retrieval of the mean volume diameter Dm and incorporating the relationship between the dual-wavelength ratio DWRS/Ka and Dm. Those techniques allow for retrievals from single millimeter-wavelength radar measurements and do not necessarily require the dual-wavelength ratio (DWR) measurements, if the DWR–Dm relation and Rayleigh assumption for Ka-band KDP are valid. Comparison between the quasivertical profile product obtained from KASPR and the columnar vertical profile product generated from the nearby WSR-88D S-band radar measurements demonstrates that the DWRS/Ka can be estimated from the two close radars without the need for collocated radar beams and synchronized antenna scanning and can be used for determining the Rayleigh-equivalent value of Z. The performance of the suggested techniques is evaluated for seven winter storms using surface disdrometer and snow accumulation measurements. Significance Statement Ice water content (IWC) estimation using millimeter-wavelength radar measurements has been challenging for decades, because of the complexity of snow particle properties and size, which can cause complex scattering at the shorter radar wavelengths. The suggested polarimetric techniques overcome this difficulty via utilizing specific differential phase KDP which is higher at millimeter wavelengths than at centimeter wavelengths. This study proposes new IWC relationships for Ka-band polarimetric radar measurements and evaluates them using a Ka-band Scanning Polarimetric Radar (KASPR) and a nearby NEXRAD (S-band) polarimetric radar for the U.S. northeast coast winter storms. The proposed techniques can be applied to other millimeter-wavelength radars and shed light on the millimeter-wavelength polarimetric radar IWC estimation.

Soil Roughness Measuring System Combined With Image Processing