Abstract

A rainfall nowcasting system is developed that identifies locations of raining clouds on consecutive infrared geosynchronous satellite images while predicting the movement of the rain cells for up to 10 h using cloud-motion-based winds. As part of the analysis, the strengths and weaknesses of various kinds of cloud wind filtering schemes and both steady and nonsteady storm advection techniques as forecast operators for quantitative precipitation forecasting are evaluated. The first part of the study addresses the development of a probability matching method (PMM) between histograms of equivalent blackbody temperatures (EBBTs) and Special Sensor Microwave Imager (SSM/I)–derived rain rates (RRs), which enables estimating RRs from instantaneous infrared imagery and allows for RR forecasts from the predicted EBBT fields. The second part of the study addresses the development and testing of the nowcasting system built upon the PMM capability and analyzes its success according to various skill score metrics. Key processes involved in the nowcasting system include the retrieved cloud-motion wind field, the filtered cloud-motion wind field, and the forecasting of a future rain field by storm advection and EBBT tendencies. These processes allow for the short-term forecasting of cloud and rain locations and of rain intensity, using PMM-based RRs from different datasets of infrared Geostationary Meteorological Satellite (GMS) and Geostationary Operational Environmental Satellite (GOES) imagery. For this study, three convective rain sequences from the Caribbean basin, the Amazon basin, and the Korean peninsula are analyzed. The final part of the study addresses the decay of forecast accuracy with time (i.e., the point at which the asymptotic limit on forecast skill is reached). This analysis indicates that the nowcasting system can produce useful rainfall forecast information out to approximately 6 h.

A long-planned field-measurement program to determine surface-energy budgets at two sites in Tibet was carried out during June 1986 in collaboration with scientists from the State Meteorological Administration, Academy of Meteorological Sciences, People's Republic of China. The data set obtained in Tibet is unique for this remote region of the world. The present report describes some of the experiences of the United States scientific team and its medical officer, M. Otteman of Ft. Collins, Colorado. The data are presently being archived on computer tapes. Preliminary analysis results are presented as typical examples of the conditions encountered at the two experimental sites near Lhasa (3635 m) and Nagqu (4500 m).

Abstract

Historical understanding of marine biological dynamics has been limited by sparse in situ observations and the fact that dedicated ocean color satellite remote sensing only began in 1997. From these observations, it has become clear that physical oceanography controls biological variability over seasonal to interannual time scales. To quantify how multidecadal, climate-scale patterns impact biological productivity, the strong correlation with sea surface temperature and sea surface height is utilized to reconstruct a retrospective 51-yr time series of surface chlorophyll, the pigment measured by ocean color satellites. The canonical correlation analysis statistical reconstruction demonstrates greatest skill away from land and within about 10° of the equator where chlorophyll variance is greatest and predominantly associated with El Niño–Southern Oscillation dynamics. Differences in chlorophyll patterns between east or central Pacific El Niño events are observed, with larger declines east of 180° for east Pacific events and west of 180° for central Pacific events. Additionally, small but significant decadal variations in chlorophyll patterns are observed corresponding to the Pacific decadal oscillation. Decadal changes in chlorophyll west of 180° are consistent with increased stratification, whereas changes between 110°–140°W may be related to long-term shoaling of the nutrient-bearing equatorial undercurrent.

Abstract

Precipitation estimation from passive microwave radiometry based on physically based profile retrieval algorithms must be aided by a microphysical generator providing structure information on the lower portions of the cloud, consistent with the upper-cloud structures that are sensed. One of the sources for this information is mesoscale model simulations involving explicit or parameterized microphysics. Such microphysical information can be then associated to brightness temperature signatures by using radiative transfer models, forming what are referred to as cloud–radiation databases. In this study cloud–radiation databases from three different storm simulations involving two different mesoscale models run at cloud scales are developed and analyzed. Each database relates a set of microphysical profile realizations describing the space–time properties of a given precipitating storm to multifrequency brightness temperatures associated to a measuring radiometer. In calculating the multifrequency signatures associated with the individual microphysical profiles over model space–time, the authors form what are called brightness temperature model manifolds. Their dimensionality is determined by the number of frequencies carried by the measuring radiometer. By then forming an analogous measurement manifold based on the actual radiometer observations, the radiative consistency between the model representation of a rain cloud and the measured representation are compared. In the analysis, the authors explore how various microphysical, macrophysical, and environmental factors affect the nature of the model manifolds, and how these factors produce or mitigate mismatch between the measurement and model manifolds. Various methods are examined that can be used to eliminate such mismatch. The various cloud–radiation databases are also used with a simplified profile retrieval algorithm to examine the sensitivity of the retrieved hydrometeor profiles and surface rainrates to the different microphysical, macrophysical, and environmental factors of the simulated storms. The results emphasize the need for physical retrieval algorithms to account for a number of these factors, thus preventing biased interpretation of the rain properties of precipitating storms, and minimizing rms uncertainties in the retrieved quantities.

Abstract

In this first part of a two-part investigation, large-scale Geostationary Operational Environmental Satellite (GOES) analyses over the Amazônia region have been carried out for March and October of 1999 to provide detailed information on surface radiation budget (SRB) and precipitation variability. SRB fluxes and rainfall are the two foremost cloud-modulated control variables that affect land surface processes, and they require specification at space–time resolutions concomitant with the changing cloud field to represent adequately the complex coupling of energy, water, and carbon budgets. These processes ultimately determine the relative variations in carbon sequestration and carbon dioxide release within a forest ecosystem. SRB and precipitation retrieval algorithms using GOES imager measurements are used to retrieve surface downward radiation and surface rain rates at high space–time resolutions for large-scale carbon budget modeling applications in conjunction with the Large-Scale Biosphere–Atmosphere Experiment in Amazônia. To validate the retrieval algorithms, instantaneous estimates of SRB fluxes and rain rates over 8 km × 8 km areas were compared with 30-min-averaged surface measurements obtained from tower sites located near Ji-Paraná and Manaus in the states of Rondônia and Amazonas, respectively. Because of large aerosol concentrations originating from biomass burning during the dry season (i.e., September and October for purposes of this analysis), an aerosol index from the Total Ozone Mapping Spectrometer is used in the solar radiation retrieval algorithm. The validation comparisons indicate that bias errors for incoming total solar, photosynthetically active radiation (PAR), and infrared flux retrievals are under 4%, 6%, and 3% of the mean values, respectively. Precision errors at the analyzed space– time scales are on the order of 20%, 20%, and 5%. The visible and infrared satellite measurements used for precipitation retrieval do not directly detect rainfall processes, and yet they are responsive to the rapidly changing cloud fields, which are directly associated with precipitation life cycles over the Amazon basin. In conducting the validation analysis at high space–time scales, the comparisons indicate systematic bias uncertainties on the order of 25%. These uncertainties are comparable to published values from an independent assessment of bias uncertainties inherent to the current highest-quality satellite retrievals, that is, from the Tropical Rainfall Measuring Mission. Because precipitation is a weak direct control on photosynthesis for the Amazon ecosystem, that is, photosynthesis is dominated by the strong diurnal controls of incoming PAR and ambient air-canopy temperatures, such uncertainties are tolerable. By the same token, precipitation is a strong control on soil thermal properties and carbon respiration through soil moisture, but the latter is a time-integrating variable and thus inhibits introduction of modeling errors caused by random errors in the precipitation forcing. The investigation concludes with analysis of the monthly, daily, and diurnal variations intrinsic to SRB and rainfall processes over the Amazon basin, including explanations of how these variations arise during wet- and dry-season periods.

Abstract

The Southern Ocean plays a critical role in the global climate system by mediating atmosphere–ocean partitioning of heat and carbon dioxide. However, Earth system models are demonstrably deficient in the Southern Ocean, leading to large uncertainties in future air–sea CO₂ flux projections under climate warming and incomplete interpretations of natural variability on interannual to geologic time scales. Here, we describe a recent aircraft observational campaign, the O₂/N₂ Ratio and CO₂ Airborne Southern Ocean (ORCAS) study, which collected measurements over the Southern Ocean during January and February 2016. The primary research objective of the ORCAS campaign was to improve observational constraints on the seasonal exchange of atmospheric carbon dioxide and oxygen with the Southern Ocean. The campaign also included measurements of anthropogenic and marine biogenic reactive gases; high-resolution, hyperspectral ocean color imaging of the ocean surface; and microphysical data relevant for understanding and modeling cloud processes. In each of these components of the ORCAS project, the campaign has significantly expanded the amount of observational data available for this remote region. Ongoing research based on these observations will contribute to advancing our understanding of this climatically important system across a range of topics including carbon cycling, atmospheric chemistry and transport, and cloud physics. This article presents an overview of the scientific and methodological aspects of the ORCAS project and highlights early findings.

Abstract

The Rapid Refresh (RAP), an hourly updated assimilation and model forecast system, replaced the Rapid Update Cycle (RUC) as an operational regional analysis and forecast system among the suite of models at the NOAA/National Centers for Environmental Prediction (NCEP) in 2012. The need for an effective hourly updated assimilation and modeling system for the United States for situational awareness and related decision-making has continued to increase for various applications including aviation (and transportation in general), severe weather, and energy. The RAP is distinct from the previous RUC in three primary aspects: a larger geographical domain (covering North America), use of the community-based Advanced Research version of the Weather Research and Forecasting (WRF) Model (ARW) replacing the RUC forecast model, and use of the Gridpoint Statistical Interpolation analysis system (GSI) instead of the RUC three-dimensional variational data assimilation (3DVar). As part of the RAP development, modifications have been made to the community ARW model (especially in model physics) and GSI assimilation systems, some based on previous model and assimilation design innovations developed initially with the RUC. Upper-air comparison is included for forecast verification against both rawinsondes and aircraft reports, the latter allowing hourly verification. In general, the RAP produces superior forecasts to those from the RUC, and its skill has continued to increase from 2012 up to RAP version 3 as of 2015. In addition, the RAP can improve on persistence forecasts for the 1–3-h forecast range for surface, upper-air, and ceiling forecasts.

Abstract

Lateral stirring is a basic oceanographic phenomenon affecting the distribution of physical, chemical, and biological fields. Eddy stirring at scales on the order of 100 km (the mesoscale) is fairly well understood and explicitly represented in modern eddy-resolving numerical models of global ocean circulation. The same cannot be said for smaller-scale stirring processes. Here, the authors describe a major oceanographic field experiment aimed at observing and understanding the processes responsible for stirring at scales of 0.1–10 km. Stirring processes of varying intensity were studied in the Sargasso Sea eddy field approximately 250 km southeast of Cape Hatteras. Lateral variability of water-mass properties, the distribution of microscale turbulence, and the evolution of several patches of inert dye were studied with an array of shipboard, autonomous, and airborne instruments. Observations were made at two sites, characterized by weak and moderate background mesoscale straining, to contrast different regimes of lateral stirring. Analyses to date suggest that, in both cases, the lateral dispersion of natural and deliberately released tracers was O(1) m² s^–1 as found elsewhere, which is faster than might be expected from traditional shear dispersion by persistent mesoscale flow and linear internal waves. These findings point to the possible importance of kilometer-scale stirring by submesoscale eddies and nonlinear internal-wave processes or the need to modify the traditional shear-dispersion paradigm to include higher-order effects. A unique aspect of the Scalable Lateral Mixing and Coherent Turbulence (LatMix) field experiment is the combination of direct measurements of dye dispersion with the concurrent multiscale hydrographic and turbulence observations, enabling evaluation of the underlying mechanisms responsible for the observed dispersion at a new level.

Abstract

The years since 2000 have been a golden age in in situ ocean observing with the proliferation and organization of autonomous platforms such as surface drogued buoys and subsurface Argo profiling floats augmenting ship-based observations. Global time series of mean sea surface temperature and ocean heat content are routinely calculated based on data from these platforms, enhancing our understanding of the ocean’s role in Earth’s climate system. Individual measurements of meteorological, sea surface, and subsurface variables directly improve our understanding of the Earth system, weather forecasting, and climate projections. They also provide the data necessary for validating and calibrating satellite observations. Maintaining this ocean observing system has been a technological, logistical, and funding challenge. The global COVID-19 pandemic, which took hold in 2020, added strain to the maintenance of the observing system. A survey of the contributing components of the observing system illustrates the impacts of the pandemic from January 2020 through December 2021. The pandemic did not reduce the short-term geographic coverage (days to months) capabilities mainly due to the continuation of autonomous platform observations. In contrast, the pandemic caused critical loss to longer-term (years to decades) observations, greatly impairing the monitoring of such crucial variables as ocean carbon and the state of the deep ocean. So, while the observing system has held under the stress of the pandemic, work must be done to restore the interrupted replenishment of the autonomous components and plan for more resilient methods to support components of the system that rely on cruise-based measurements.

Abstract

The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.