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The Mount Washington Observatory Regional Mesonet (MWRM) is a network of 18 remote meteorological monitoring stations (as of 2022), including the Auto Road Vertical Profile (ARVP), located across the White Mountains of northern New Hampshire. Each station measures temperature and relative humidity, with additional variables at many locations. All stations need to withstand the frequent combination of intense cold, high precipitation amounts, icing, and hurricane-force winds in a mountain environment. Due to these challenges, the MWRM employs rugged instrumentation, an innovative radio-communications relay approach, and carefully selected sites that balance ideal measuring environments with station survivability. Data collected from the MWRM are used operationally by forecasters (including Mount Washington Observatory and National Weather Service staff) to validate model guidance, by alpine and climate scientists, by recreationalists accessing conditions in the backcountry, by groups operating on the mountain (Cog Railway, toll Auto Road), and by search and rescue organizations. This paper provides a detailed description of the network, with emphasis on how the challenging climate and terrain of this mountain region impacts sensor selection, site maintenance, and overall operation. Significance Statement: The mountain environment is a heterogeneous landscape, and interactions between the atmosphere and terrain can cause a wide variety of conditions across time and space. Our network of remote stations at different elevations across the White Mountains allows data users to understand how the weather varies spatially across the mountain range where conditions on higher peaks can be drastically, and dangerously, different. Sharing information about the MWRM can help other groups establish networks in similar challenging environments, and broaden our understanding of weather and climate in mountainous regions. [ABSTRACT FROM AUTHOR]

First-year ice has replaced multiyear ice in the Northern Sea Route area since 2008. In this area, sea ice survival during summer substantially depends on first-year ice thickness at melt onset, and thus monitoring of first-year ice thickness in the freezing period is a key to forecasting sea ice distributions in the following summer. In this paper we introduce a new algorithm to estimate flat first-year ice draft using brightness temperature data measured by the Advanced Microwave Scanning Radiometer-2 (AMSR2). The algorithm uses a gradient ratio (GR) of 18- and 36-GHz vertically polarized brightness temperatures based on decreases in sea ice emissivity in higher AMSR2 frequency channels with thermodynamic growth associated with an increase in volume scattering. Such spectral characteristics of the emissivity are examined by comparing GR values with flat first-year ice draft extracted by mode values of in situ draft data measured by a moored ice profiling sonar. The accuracy of the daily draft estimated from GR values after applying proper noise filters is about 10 cm for a draft range of 0.4–1.2 m. [ABSTRACT FROM AUTHOR]

The Nebraska Mesonet was established in 1981 as one of the nation's first automated state weather networks. "Automated" is defined by the nature of the observations being made and recorded by machine, as opposed to observations made and recorded manually. At the time of inception, the five observing locations were geared toward servicing agricultural production applications. The Nebraska Mesonet has grown to 69 stations (as of 2018) and is now a multipurpose environmental observing network under the Nebraska State Climate Office (NSCO). The network is composed of environmental observation stations, sited using best practices for mesoscale and microscale environment situations. Precise observations are acquired using high-quality instrumentation, following manufacturer recommendations for calibrations and maintenance. Calibrations are performed in the NSCO calibration laboratory. Uses for the data include but are not limited to water management, drought monitoring, energy production, health, environmental research, animal management, and crop pest management. This paper provides a technical overview and history of the network, outlining current practices for station siting, maintenance, data quality assurance, and data utility. [ABSTRACT FROM AUTHOR]

The T5 expendable bathythermographs reach the greatest depth within the current XBT family. Since the early 1970s, in several areas they have been providing a significant part of available temperature profiles below 1000 m and therefore represent an important resource for ocean climate study. In this paper we present new results from laboratory tests of Sippican T5 and T5/20 probes and analyses of more than 350 XBT–CTD matched pairs from our own field trials and the World Ocean Database (WOD), and we propose an improved fall rate equation (coefficients: A = 6.720 ± 0.025 m s−1, B = 0.001 60 ± 0.000 15 m s−2, Offset = 1.00 ± 0.65 m). Possible influences of probe physical characteristics and initial launch conditions on the probe motion have also been investigated with launching height and probe weight being identified as important factors. Analyses also confirm that fall speed and pure temperature error increase with water temperature, as previously reported for other XBT types. The uncertainties in depth and temperature measurements are then calculated. Finally, a new correction for a global T5 dataset is proposed, with an update of the currently available schemes. [ABSTRACT FROM AUTHOR]

The NOAA Tropical Atmosphere Ocean (TAO) moored array has, for three decades, been a valuable resource for monitoring and forecasting El Niño-Southern Oscillation and understanding physical oceanographic as well as coupled processes in the tropical Pacific influencing global climate. Acoustic Doppler current profiler (ADCP) measurements by TAO moorings provide benchmarks for evaluating numerical simulations of subsurface circulation including the Equatorial Undercurrent (EUC). Meanwhile, the Sea Education Association (SEA) has been collecting data during repeat cruises to the central equatorial Pacific Ocean (160°-126°W) throughout the past decade that provide useful cross validation and quantitative insight into the potential for stationary observing platforms such as TAO to incur sampling biases related to the strength of the EUC. This paper describes some essential sampling characteristics of the SEA dataset, compares SEA and TAO velocity measurements in the vicinity of the EUC, shares new insight into EUC characteristics and behavior only observable in repeat cross-equatorial sections, and estimates the sampling bias incurred by equatorial TAO moorings in their estimates of the velocity and transport of the EUC. The SEA high-resolution ADCP dataset compares well with concurrent TAO measurements (RMSE = 0.05 m s−1; R2 = 0.98), suggests that the EUC core meanders sinusoidally about the equator between ±0.4° latitude, and reveals a mean sampling bias of equatorial measurements (e.g., TAO) of the EUC's zonal velocity of −0.14 ± 0.03 m s−1 as well as a ~10% underestimation of EUC volume transport. A bias-corrected monthly record and climatology of EUC strength at 140°W for 1990-2010 is presented. [ABSTRACT FROM AUTHOR]

The recently developed two-dimensional variational methods for analyzing vortex winds from radar-observed mesocyclones can be extended to analyze three-dimensional vortex winds, but the first task for this extension is to estimate the vortex center location and its continuous variations in four-dimensional space so that the horizontal location of the vortex center can be expressed as a continuous function of height and time. To accomplish this task, a three-step method is developed in this paper. The method is applied to the Moore, Oklahoma, tornadic mesocyclone observed by the operational KTLX radar (Oklahoma City, Oklahoma) and the NSSL phased-array radar on 20 May 2013. The estimated vortex center trajectory at the ground level is verified with the tornado damage survey data. The estimated vortex center trajectories above the ground (up to 4-km height) reveal that the vortex core was initially tilted northeastward along the direction of the environmental flow and its vertical shear but became nearly vertical about 16 min later and 4 min before the vortex started to cause EF5 damages. [ABSTRACT FROM AUTHOR]

The 35-yr NOAA Advanced Very High Resolution Radiometer (AVHRR) observation record offers an excellent opportunity to study decadal climate variability, provided that all participating AVHRR instruments are calibrated on a consistent radiometric scale. Because of the lack of onboard calibration systems, the solar imaging channels of the AVHRR must be vicariously calibrated using invariant Earth targets as a calibrated reference source. The greatest challenge in calibrating the AVHRR dataset is the orbit degradation of the NOAA satellites, which eventually drift into a terminator orbit several years after launch. Therefore, the invariant targets must be characterized over the full range of solar zenith angles (SZAs) sampled by the satellite instrument. This study outlines a multiple invariant Earth target calibration approach specifically designed to account for the degrading NOAA orbits. The desert, polar ice, and deep convective cloud (DCC) invariant targets are characterized over all observed SZAs using NOAA-16 AVHRR measurements, which are referenced to the Aqua MODIS Collection 6 calibration via direct transfer of the MODIS calibration to the NOAA-16 AVHRR instrument using simultaneous nadir overpass (SNO) observations over the North Pole. The multiple invariant target calibrations are combined using the inverse of their temporal variance to optimize the resulting calibration stability. The NOAA-18 AVHRR gains derived using the desert, polar ice, and DCC targets, as well as from SNO, were found consistent within 1%, thereby validating that the Aqua MODIS calibration is effectively transferred to the reference calibration targets. The companion paper, Part II, applies the methodology across the AVHRR record to derive the sensor-specific calibration coefficients. [ABSTRACT FROM AUTHOR]

This paper describes a cloud type radiance record derived from NOAA polar-orbiting weather satellites using cloud properties retrieved from the Advanced Very High Resolution Radiometer (AVHRR) and spectral brightness temperatures ( T b) observed by the High Resolution Infrared Radiation Sounder (HIRS). The authors seek to produce a seamless, global-scale, long-term record of cloud type and T b statistics intended to better characterize clouds from seasonal to decadal time scales. Herein, the methodology is described in which the cloud type statistics retrieved from AVHRR are interpolated onto each HIRS footprint using two cloud classification methods. This approach is tested over the northeast tropical and subtropical Pacific Ocean region, which contains a wide variety of cloud types during a significant ENSO variation from 2008 to 2009. It is shown that the T b histograms sorted by cloud type are realistic for all HIRS channels. The magnitude of T b biases among spatially coincident satellite intersections over the northeast Pacific is a function of cloud type and wavelength. While the sign of the bias can change, the magnitudes are generally small for NOAA-18 and NOAA-19, and NOAA-19 and MetOp-A intersections. The authors further show that the differences between calculated standard deviations of cloud-typed T b well exceed intersatellite calibration uncertainties. The authors argue that consideration of higher-order statistical moments determined from spectral infrared observations may serve as a useful long-term measure of small-scale spatial changes, in particular cloud types over the HIRS-AVHRR observing record. [ABSTRACT FROM AUTHOR]

We present a new global oxygen atlas. This atlas uses all of the available full water column profiles of oxygen, salinity, and temperature available as part of the World Ocean Database released in 2018. Instead of optimal interpolation, we use the Data Interpolating Variational Analysis (DIVA) approach to map the available profiles onto 108 depth levels between the surface and 6800 m, covering more than 99% of ocean volume. This 1/2° × 1/2° atlas covers the period 1955–2018 in 1-yr intervals. The DIVA method has significant benefits over traditional optimal interpolation. It allows the explicit inclusion of advection and boundary constraints, thus offering improvements in the representations of oxygen, salinity, and temperature in regions of strong flow and near coastal boundaries. We demonstrate these benefits of this mapping approach with some examples from this atlas. We can explore the regional and temporal variations of oxygen in the global oceans. Preliminary analyses confirm earlier analyses that the oxygen minimum zone in the eastern Pacific Ocean has expanded and intensified. Oxygen inventory changes between 1970 and 2010 are assessed and compared against prior studies. We find that the full ocean oxygen inventory decreased by 0.84% ± 0.42%. For this period, temperature-driven solubility changes explain about 21% of the oxygen decline over the full water column; in the upper 100 m, solubility changes can explain all of the oxygen decrease; for the 100–600 m depth range, it can explain only 29%, 19% between 600 and 1000 m, and just 11% in the deep ocean. Significance Statement: The purpose of this study is to create a new oxygen atlas of the world's oceans using a technique that better represents the effects of ocean currents and topographic boundaries, and to investigate how oxygen in the ocean has changed over recent decades. We find the total quantity of oxygen in the world's oceans has decreased by 0.84% since 1970, similar to previous studies. We also examine how much of this change can be explained by changes in water temperature; we find that this can explain all the changes in the upper 100 m but only 21% of the oxygen decline over the whole water column. [ABSTRACT FROM AUTHOR]

Nansen bottle casts served as the main oceanographic instrumentation type for more than a century since the establishing of the technique in the late 1890s. Between the end of the 1960s and the end of the 1990s Nansen cast technique has been gradually replaced by electronic sensor profilers (CTD). Both instrumentation types are considered as the most accurate among other oceanographic instruments and are often used as the unbiased reference. We conducted a comprehensive investigation of the consistency of the temperature data from Nansen casts and CTD profilers analyzing the quasi-collocated bottle and CTD data between the 1960s and the 1990s when both instrumentation types overlap. We found that Nansen casts tend to overestimate the sample depth with reversing mercury-in-glass thermometer temperatures being on average slightly lower compared to CTD data. Respectively, depth and temperature corrections are provided. Further, we estimated the ocean heat content changes between 1955 and 1990 using (along with all other instrumentation types) corrected and uncorrected Nansen cast data. These calculations show that for the upper 2 km layer the global average warming trend for this time period increases from 0.20 ± 0.05 W m−2 for the uncorrected data to 0.28 ± 0.06 W m−2 for the corrected data at the 90% confidence level. Finally, we suggest that the Nansen bottle cast profiles be put into a separate instrumentation group within the World Ocean Database. [ABSTRACT FROM AUTHOR]

The provision of reliable results from numerical wave models implemented over vast ocean areas can be considered as a time-consuming process. In this regard, the estimation of areas with maximum similarity in wave climate spatial areas and the determination of associated representative point locations for these areas can play an important role in climate research and in engineering applications. To deal with this issue, we apply a state-of-the-art clustering method, Geo-SOM, to determine geographical areas with similar wave regimes, in terms of mean wave direction (MWD), mean wave period (T0), and significant wave height (Hs). Although this method has many strengths, a weakness is related to detection and accounting of the most extreme and rare events. To resolve this deficiency, an initial preprocessing method (called PG-Geo-SOM) is applied. To evaluate the performance of this method, extreme wave parameters, including Hs and T0, are calculated. We simulate the present climate, represented as 1979 to 2017, compared to the future climate, 2060–98, following the Intergovernmental Panel on Climate Change (IPCC) future scenario RCP8.5 in the northwestern Atlantic Ocean. In this approach, the wave parameter data are divided into distinct groups, or clusters, motivated by their geographical positions. For each cluster, the centroid spatial point and the time series of data are extracted, for Hs, MWD, and T0. Extreme values are estimated for 5-, 10-, 25-, 50-, and 100-yr return periods, using Gumbel, exponential, and Weibull stochastic models, for both present and future climates. Results show that for parameter T0, the impact of climate change for the study area is a decreasing trend, while for Hs, in coastal and shelf areas up to about 1000 km from the coastline, increasing trends are estimated, and in open-ocean areas, far from the coast, decreasing trends are obtained. [ABSTRACT FROM AUTHOR]

Expendable bathythermographs (XBTs) have been widely deployed for ocean monitoring since the late 1960s. Improving the quality of XBT data is a vital task in climatology. Many factors (e.g., temperature, probe type, and manufacturing time) have been identified as major influences of XBT systematic bias. In addition, the recording system (RS) has long been suspected as another factor. However, this factor has not been taken into account in any global XBT correction schemes, partly because its impact is poorly understood. Here, based on analysis of an XBT–CTD side-by-side dataset and a global collocated reference dataset, the influence of RSs on the pure temperature error (PTE) is examined. Results show a clear time dependency of PTE on the RS, with maximum values occurring in the 1970s. In addition, the method used to convert thermistor resistance into temperature in the RS (using a resistance–temperature equation) has changed over time. These changes, together with the decadal changes in RSs, might contribute a small error (10% on average) to the RS dependency. Here, an improvement of global XBT bias correction that accounts for the RS dependency is proposed. However, more than 70% of historical global XBT data are missing RS-type information. We investigate several assumptions about the temporal distribution of RS types, and all scenarios lead to at least a ~50% reduction in the time variation of PTE compared with the uncorrected data. Therefore, the RS dependency should be taken into account in updated XBT correction schemes, which would have further implications for climatology studies. [ABSTRACT FROM AUTHOR]

Snow depth is an important geophysical variable for investigating sea ice and climate change, which can be obtained from satellite data. However, there is a large number of missing data in satellite observations of snow depth. In this study, a methodology, the periodic functions fitting with varying parameter (PFF-VP), is presented to fit the time series of snow depth on Arctic sea ice obtained from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). The time-varying parameters are obtained by the independent point (IP) scheme and cubic spline interpolation. The PPF-VP is validated by experiments in which part of the observations are artificially removed and used to compare with the fitting results. Results indicate that the PPF-VP performs better than three traditional fitting methods, with its fitting results closer to observations and with smaller errors. In the practical experiments, the optimal number of IPs can be determined by only considering the fraction of missing data, particularly the length of the longest gaps in the snow-depth time series. All the experimental results indicate that the PPF-VP is a feasible and effective method to fit the time series of snow depth and can provide continuous data of snow depth for further study. [ABSTRACT FROM AUTHOR]

Screen-level air temperature measurements at surface observation stations are influenced by local-site-scale factors. These local influences may affect global-scale climate change studies. This study investigated the influence of surface obstacles on air temperature measurements at the screen level at climate observation stations in Japan. Screen-level air temperature was measured simultaneously at two neighboring sites (<100 m apart) that differed in terms of their openness. Daytime air temperature was 0°–1°C higher at the narrower site, and theoretical analysis revealed that this warming was caused by poor ventilation. At night, poor ventilation at the narrower site caused the air temperature to be 0°–0.2°C lower, which was demonstrated experimentally and by theoretical analysis. The range of temperature changes due to site narrowing shown in this study is not negligible in climate change studies. Guidelines for site maintenance and metadata recoding were consequently proposed in terms of site openness. [ABSTRACT FROM AUTHOR]

Researchers now have the benefit of an unprecedented suite of space- and ground-based sensors that provide multidimensional and multiparameter precipitation information. Motivated by NASA's Global Precipitation Measurement (GPM) mission and ground validation objectives, the System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) has been developed as a unique multisensor precipitation data fusion tool to unify field observations recorded in a variety of formats and coordinate systems into a common reference frame. Through platform-specific modules, SIMBA processes data from native coordinates and resolutions only to the extent required to set them into a user-defined three-dimensional grid. At present, the system supports several ground-based scanning research radars, NWS NEXRAD radars, profiling Micro Rain Radars (MRRs), multiple disdrometers and rain gauges, soundings, the GPM Microwave Imager and Dual- Frequency Precipitation Radar on board the Core Observatory satellite, and Multi-Radar Multi-Sensor system quantitative precipitation estimates. SIMBA generates a new atmospheric column data product that contains a concomitant set of all available data from the supported platforms within the user-specified grid defining the column area in the versatile netCDF format. Key parameters for each data source are preserved as attributes. SIMBA provides a streamlined framework for initial research tasks, facilitating more efficient precipitation science. We demonstrate the utility of SIMBA for investigations, such as assessing spatial precipitation variability at subpixel scales and appraising satellite sensor algorithm representation of vertical precipitation structure for GPM Core Observatory overpass cases collected in the NASAWallops Precipitation Science Research Facility and the GPM Olympic Mountain Experiment (OLYMPEX) ground validation field campaign in Washington State. [ABSTRACT FROM AUTHOR]

Precipitation observation with the Tropical Rainfall Measuring Mission's (TRMM's) precipitation radar (PR) lasted for more than 17 years. To study the changes in the water and energy cycle related to interannual and decadal variabilities of climate, homogeneity of long-term PR data is essential. The aim of the study is to develop a precipitation climate record from the 17-yr PR observation. The focus was on mitigating the discontinuities associated with the switching to redundant electronics in the PR in June 2009. In version 7 of the level-1 PR product, a discontinuity in noise power is found at this timing, indicating a change in the signal-to-noise ratio. To mitigate the effect of this discontinuity on climate studies, the noise power of the B-side PR obtained after June 2009 is artificially increased to match that of the A-side PR. Simulation results show that the storm height and the precipitation frequency detected by the PR relatively decrease by 2.17% and 5.15% in the TRMM coverage area (35°S-35°N), respectively, and that the obvious discontinuity of the time series by the storm height and the precipitation fraction caused by the switching to the redundancy electronics is mitigated. Differences in the statistics of other precipitation parameters caused by the switching are also mitigated. The unconditional precipitation rate derived from the adjusted data obtained over the TRMM coverage area decreases by 0.90% as compared with that determined from the original data. This decrease is mainly caused by reductions in the detection of light precipitation. [ABSTRACT FROM AUTHOR]

Historical reconstructions of climate fields, such as sea surface temperature (SST), are important for climate studies and monitoring. Reconstructions use statistics from a well-sampled base period to analyze a sparsely sampled historical period. Here a method is shown for adjusting the base-period statistics using the available historical data so that statistics better represent historical variations. The method is demonstrated using annual SST anomalies from a coupled GCM historical run, 1861-2005, forced by greenhouse gases and aerosols. Simulated data are constructed from the model's SST using observed historical SST sampling with error estimates added. Reconstructions are performed using the simulated data, and the results are compared to the full model SST without added errors. The results from applying other reconstruction methods to the simulated data are compared. The tests show that the method improves annual SST reconstructions, especially in the early years, when sampling is most sparse and in the extratropics. In particular, the 1881-1900 correlation averaged over 30°-60°S and over 30°-60°N improves from about 0.4 using noniterative reconstruction to about 0.6 using iterative reconstruction. The correlations of annual values in the tropics are about 0.7 with both methods. Incorporating those improvements into an SST reconstruction could better represent extratropical climate variations in the nineteenth and early twentieth centuries, and improve the value of the reconstruction for long-period climate studies and for validating climate models. [ABSTRACT FROM AUTHOR]