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Imke Durre
,
Anthony Arguez
,
Carl J. Schreck III
,
Michael F. Squires
, and
Russell S. Vose

Abstract

In this paper, a new set of daily gridded fields and area averages of temperature and precipitation is introduced that covers the contiguous United States (CONUS) from 1951 to present. With daily updates and a grid resolution of approximately 0.0417° (nominally 5 km), the product, named nClimGrid-Daily, is designed to be used particularly in climate monitoring and other applications that rely on placing event-specific meteorological patterns into a long-term historical context. The gridded fields were generated by interpolating morning and midnight observations from the Global Historical Climatology Network–Daily dataset using thin-plate smoothing splines. Additional processing steps limit the adverse effects of spatial and temporal variations in station density, observation time, and other factors on the quality and homogeneity of the fields. The resulting gridded data provide smoothed representations of the point observations, although the accuracy of estimates for individual grid points and days can be sensitive to local spatial variability and the ability of the available observations and interpolation technique to capture that variability. The nClimGrid-Daily dataset is therefore recommended for applications that require the aggregation of estimates in space and/or time, such as climate monitoring analyses at regional to national scales.

Significance Statement

Many applications that use historical weather observations require data on a high-resolution grid that are updated daily. Here, a new dataset of daily temperature and precipitation for 1951–present is introduced that was created by interpolating irregularly spaced observations to a regular grid with a spacing of 0.0417° across the contiguous United States. Compared to other such datasets, this product is particularly suitable for monitoring climate and drought on a daily basis because it was processed so as to limit artificial variations in space and time that may result from changes in the types and distribution of observations used.

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Coltin Grasmick
,
Bart Geerts
,
Jeffrey R. French
,
Samuel Haimov
, and
Robert M. Rauber

Abstract

Properties of frozen hydrometeors in clouds remain difficult to sense remotely. Estimates of number concentration, distribution shape, ice particle density, and ice water content are essential for connecting cloud processes to surface precipitation. Progress has been made with dual-frequency radars, but validation has been difficult because of lack of particle imaging and sizing observations collocated with the radar measurements. Here, data are used from two airborne profiling (up and down) radars, the W-band Wyoming Cloud Radar and the Ka-band Profiling Radar, allowing for Ka–W-band dual-wavelength ratio (DWR) profiles. The aircraft (the University of Wyoming King Air) also carried a suite of in situ cloud and precipitation probes. This arrangement is optimal for relating the “flight-level” DWR (an average from radar gates below and above flight level) to ice particle size distributions measured by in situ optical array probes, as well as bulk properties such as minimum snow particle density and ice water content. This comparison reveals a strong relationship between DWR and the ice particle median-volume diameter. An optimal range of DWR values ensures the highest retrieval confidence, bounded by the radars’ relative calibration and DWR saturation, found here to be about 2.5–7.5 dB. The DWR-defined size distribution shape is used with a Mie scattering model and an experimental mass–diameter relationship to test retrievals of ice particle concentration and ice water content. Comparison with flight-level cloud-probe data indicate good performance, allowing microphysical interpretations for the rest of the vertical radar transects.

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Martin Schön
,
Keri Anne Nicoll
,
Yann Georg Büchau
,
Stefan Chindea
,
Andreas Platis
, and
Jens Bange

Abstract

Atmospheric electricity measurements made from small unmanned aircraft systems (UAS) are rare but are of increasing interest to the atmospheric science community due to the information that they can provide about aerosol and turbulence characteristics of the atmospheric boundary layer (ABL). Here we present the first analysis of a new dataset of space charge and meteorology measurements made from the small, electric, fixed-wing UAS model MASC-3. Two distinct experiments are discussed: 1) Flights past a 99 m metal tower to test the response of the charge sensor to a fixed distortion of the electric field caused by the geometry of the tower. Excellent agreement is found between the charge sensor response from the MASC-3 and modeled electric field around the tower. 2) Vertical profiles up to an altitude of 2500 m to study the evolution of the ABL with the time of day. These flights demonstrated close agreement between the space charge profiles and temperature, relative humidity, and turbulence parameters, as would be expected on a fair-weather day with summertime convection. Maximum values of space charge measured were of order 70 pC m−3, comparable with other measurements in the literature from balloon platforms. These measurements demonstrate the suitability of small UAS for atmospheric electrical measurements, provided that care is taken over the choice of aircraft platform, sensor placement, minimization of electrical interference, and careful choice of the flight path. Such aircraft are typically more cost-effective than manned aircraft and are being increasingly used for atmospheric science purposes.

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Yuanli Fang
,
Yiping Wu
, and
Haocai Huang

Abstract

The research on deep-sea hydrothermal fluids, cold springs, and other bottom water bodies has important implications for ecosystems. But the deep-sea environment is very harsh, and many existing sampling devices cannot meet the requirements in terms of sampling purity and gas preservation capabilities. Many current samplers are basically arranged in a vertical manner, which means that a set of trigger devices need to be installed at the entrance and exit of the sampling channel, which consumes a lot of space. Taking the flowthrough deep-seawater sequence sampling mechanism as the research object, we show a horizontal flowthrough water sampler. Through numerical simulation and experimental research on the displacement mechanism of the target sample and prefilled pure water, the displacement efficiencies under different flow velocities and sampling cavity shapes were obtained. The results confirmed that the positions of the inlet and outlet and the shapes of the sampling cavity have little influence on the displacement efficiencies at high flow rates. However, installing the inlet below the sampling cavity and installing the outlet above the sampling cavity can significantly reduce the blind area of displacement. Setting a small inclination angle to the capsule sampling cavity helps to improve the displacement effect at low flow rates. This design and research results not only simplified the complicated trigger mechanism of the traditional vertical water samplers, but also provided a reference for the operation modes of the samplers under different sample conditions.

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Ryan C. Scott
,
Fred G. Rose
,
Paul W. Stackhouse Jr.
,
Norman G. Loeb
,
Seiji Kato
,
David R. Doelling
,
David A. Rutan
,
Patrick C. Taylor
, and
William L. Smith Jr.

Abstract

Satellite observations from Clouds and the Earth’s Radiant Energy System (CERES) radiometers have produced over two decades of world-class data documenting time–space variations in Earth’s top-of-atmosphere (TOA) radiation budget. In addition to energy exchanges among Earth and space, climate studies require accurate information on radiant energy exchanges at the surface and within the atmosphere. The CERES Cloud Radiative Swath (CRS) data product extends the standard Single Scanner Footprint (SSF) data product by calculating a suite of radiative fluxes from the surface to TOA at the instantaneous CERES footprint scale using the NASA Langley Fu–Liou radiative transfer model. Here, we describe the CRS flux algorithm and evaluate its performance against a network of ground-based measurements and CERES TOA observations. CRS all-sky downwelling broadband fluxes show significant improvements in surface validation statistics relative to the parameterized fluxes on the SSF product, including a ∼30%–40% (∼20%) reduction in SW↓ (LW↓) root-mean-square error (RMSΔ), improved correlation coefficients, and the lowest SW↓ bias over most surface types. RMSΔ and correlation statistics improve over five different surface types under both overcast and clear-sky conditions. The global mean computed TOA outgoing LW radiation (OLR) remains within <1% (2–3 W m−2) of CERES observations, while the global mean reflected SW radiation (RSW) is excessive by ∼3.5% (∼9 W m−2) owing to cloudy-sky computation errors. As we highlight using data from two remote field campaigns, the CRS data product provides many benefits for studies requiring advanced surface radiative fluxes.

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A. Addison Alford
,
Michael I. Biggerstaff
,
Conrad L. Ziegler
,
David P. Jorgensen
, and
Gordon D. Carrie

Abstract

Mobile weather radars at high frequencies (C, X, K, and W bands) often collect data using staggered pulse repetition time (PRT) or dual pulse repetition frequency (PRF) modes to extend the effective Nyquist velocity and mitigate velocity aliasing while maintaining a useful maximum unambiguous range. These processing modes produce widely dispersed “processor” dealiasing errors in radial velocity estimates. The errors can also occur in clusters in high shear areas. Removing these errors prior to quantitative analysis requires tedious manual editing and often produces “holes” or regions of missing data in high signal-to-noise areas. Here, data from three mobile weather radars were used to show that the staggered PRT errors are related to a summation of the two Nyquist velocities associated with each of the PRTs. Using observations taken during a mature mesoscale convective system, a landfalling tropical cyclone, and a tornadic supercell storm, an algorithm to automatically identify and correct staggered PRT processor errors has been developed and tested. The algorithm creates a smooth profile of Doppler velocities using a Savitzky–Golay filter independently in radius and azimuth and then combined. Errors are easily identified by comparing the velocity at each range gate to its smoothed counterpart and corrected based on specific error characteristics. The method improves past dual PRF correction methods that were less successful at correcting “grouped” errors. Given the success of the technique across low, moderate, and high radial shear regimes, the new method should improve research radar analyses by affording the ability to retain as much data as possible rather than manually or objectively removing erroneous velocities.

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Werner E. Cook
and
J. Scott Greene

Abstract

Daily rainfall accumulation estimates have been derived from 1-min volume data collected via self-syphon rain gauges deployed in the Tropical Atmosphere–Ocean (TAO) array of oceanographic buoys. The underlying high-resolution volume data were obtained directly from the Global Tropical Moored Buoy Array (GTMBA) Project Office of NOAA/Pacific Marine Environmental Laboratory. The derived accumulations have been incorporated into the Pacific Rainfall (PACRAIN) database as estimated daily values to augment existing sea level oceanic rainfall records gathered using traditional rain gauges. They have also been included in the PACRAIN historical, monthly gridded rainfall product. The methodology presented, which employs differencing of least squares–regressed sensor levels about 0000 UTC and rain gauge syphon events, is shown to offer improved error characteristics over the methodology used to compute previously published GTMBA rain rates. In particular, the PACRAIN method yields larger coefficients of determination and smaller standard errors than the duplicated GTMBA method when applied to synthetic rainfall data with noise magnitude and decorrelation times encompassing those observed in the real 1-min data. These results are shown to be consistent with mathematical expectations. Sources of instrument and catchment errors, as well as evaporation, are discussed in the context of their potential effects on accumulation estimates for periods of a day or longer.

Significance Statement

In this paper, we describe the derivation of daily rainfall amounts from raw rain gauge data obtained from buoy-mounted rain gauges. These new accumulation estimates expand the store of rainfall estimates from locations approximating the open-ocean conditions of the tropical Pacific Ocean. The derivation technique we describe exhibits better performance than the method used to generate previously published estimates using the same dataset.

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Raphael Dussin

Abstract

A novel method to adjust the precipitation produced by atmospheric reanalyses using observational constraints to force ocean models is described. The method allows the preservation of the qualities of the high-resolution and high-frequency output from the reanalyses while eliminating their bias and spurious trends. The method is shown to be robust to degradation in both space and time of the observation dataset. This method is applied to the ERA-Interim precipitation dataset using the Global Precipitation Climatology Project (GPCP) v2.3 as the observational reference in order to create a debiased dataset that can be used to force ocean models. The produced debiased dataset is then compared to ERA-Interim and GPCP in a suite of forced ice–ocean numerical experiments using the GFDL OM4 model. Ocean states obtained with the new precipitation dataset are consistent with results from GPCP-forced experiments with respect to global metrics but produces the extra sea surface salinity variability at the time scales unresolved by the observation-based dataset. Discrepancies between modeled and observed freshwater fluxes are discussed as well as the strategies to mitigate them and their impacts.

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Caroline Comby
,
Stéphanie Barrillon
,
Jean-Luc Fuda
,
Andrea M. Doglioli
,
Roxane Tzortzis
,
Gérald Grégori
,
Melilotus Thyssen
, and
Anne A. Petrenko

Abstract

Vertical velocities knowledge is essential to study fine-scale dynamics in the surface layers of the ocean and to understand their impact on biological production mechanisms. However, these vertical velocities have long been neglected, simply parameterized, or considered as not measurable, due mainly to their order of magnitude (less than mm s−1 up to cm s−1), generally much lower than the one of the horizontal velocities (cm s−1 to dm s−1), hence the challenge of their in situ measurement. In this paper, we present an upgraded method for direct in situ measurement of vertical velocities using data from different acoustic Doppler current profilers (ADCPs) associated with CTD probes, and we perform a comparative analysis of the results obtained by this method. The analyzed data were collected during the FUMSECK cruise, from three ADCPs: two Workhorse (conventional ADCPs), one lowered on a carousel and the other deployed in free-fall mode, and one Sentinel V (a new-generation ADCP with four classical beams and a fifth vertical beam), also lowered on a carousel. Our analyses provide profiles of vertical velocities on the order of mm s−1, as expected, with standard deviations of a few mm s−1. While the fifth beam of the Sentinel V exhibits a better accuracy than conventional ADCPs, the free-fall technique provides a more accurate measurement compared to the carousel technique. Finally, this innovative study opens up the possibility to perform simple and direct in situ measurements of vertical velocities, coupling the free-fall technique with a five-beam ADCP.

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Michael Dixon
and
Ulrike Romatschke

Abstract

The Echo Classification from COnvectivity (ECCO) algorithm identifies convective and stratiform types of radar echo in three dimensions. It is based on the calculation of reflectivity texture—a combination of the intensity and the heterogeneity of the radar echoes on each horizontal plane in a 3D Cartesian volume. Reflectivity texture is translated into convectivity, which is designed to be a quantitative measure of the convective nature of each 3D radar grid point. It ranges from 0 (100% stratiform) to 1 (100% convective). By thresholding convectivity, a more traditional qualitative categorization is obtained, which classifies radar echoes as convective, mixed, or stratiform. In contrast to previous algorithms, these echo-type classifications are provided on the full 3D grid of the reflectivity field. The vertically resolved classifications, in combination with temperature data, allow for subclassifications into shallow, mid-, deep, and elevated convective features, and low, mid-, and high stratiform regions—again in three dimensions. The algorithm was validated using datasets collected over the U.S. Great Plains during the PECAN field campaign. An analysis of lightning counts shows ∼90% of lightning occurring in regions classified as convective by ECCO. A statistical comparison of ECCO echo types with the well-established GPM radar precipitation-type categories show 84% (88%) of GPM stratiform (convective) echo being classified as stratiform (convective) or mixed by ECCO. ECCO was applied to radar grids for the continental United States, the United Arab Emirates, Australia, and Europe, illustrating its robustness and adaptability to different radar grid characteristics and climatic regions.

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