NOAA National Weather Service

We present the impact tests that preceded the most recent operational upgrades to the land surface model used in the National Centers for Environmental Prediction (NCEP) mesoscale Eta model, whose operational domain includes North America. These improvements consist of changes to the “Noah” land surface model (LSM) physics, most notable in the area of cold season processes. Results indicate improved performance in forecasting low‐level temperature and humidity, with improvements to (or without affecting) the overall performance of the Eta model quantitative precipitation scores and upper air verification statistics. Remaining issues that directly affect the Noah LSM performance in the Eta model include physical parameterizations of radiation and clouds, which affect the amount of available energy at the surface, and stable boundary layer and surface layer processes, which affect surface turbulent heat fluxes and ultimately the surface energy budget.

This first paper of the two-part series describes the objectives of the community efforts in improving the Noah land surface model (LSM), documents, through mathematical formulations, the augmented conceptual realism in biophysical and hydrological processes, and introduces a framework for multiple options to parameterize selected processes (Noah-MP). The Noah-MP's performance is evaluated at various local sites using high temporal frequency data sets, and results show the advantages of using multiple optional schemes to interpret the differences in modeling simulations. The second paper focuses on ensemble evaluations with long-term regional (basin) and global scale data sets. The enhanced conceptual realism includes (1) the vegetation canopy energy balance, (2) the layered snowpack, (3) frozen soil and infiltration, (4) soil moisture-groundwater interaction and related runoff production, and (5) vegetation phenology. Sample local-scale validations are conducted over the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site, the W3 catchment of Sleepers River, Vermont, and a French snow observation site. Noah-MP shows apparent improvements in reproducing surface fluxes, skin temperature over dry periods, snow water equivalent (SWE), snow depth, and runoff over Noah LSM version 3.0. Noah-MP improves the SWE simulations due to more accurate simulations of the diurnal variations of the snow skin temperature, which is critical for computing available energy for melting. Noah-MP also improves the simulation of runoff peaks and timing by introducing a more permeable frozen soil and more accurate simulation of snowmelt. We also demonstrate that Noah-MP is an effective research tool by which modeling results for a given process can be interpreted through multiple optional parameterization schemes in the same model framework. Copyright © 2011 by the American Geophysical Union.

The system of objective weather map analysis used at the Joint Numerical Weather Prediction Unit is described. It is an integral part of the automatic data processing system, and is designed to operate with a minimum of manual supervision. The analysis method, based mainly on the method of Bergthórssen and Dööos, is essentially a method of applying corrections to a first guess field. The corrections are determined from a comparison of the data with the interpolated value of the guess field at the observation point. For the analysis of the heights of a pressure surface the reported wind is taken into account in determining the lateral gradient of the correction to be applied. A series of scans of the field is made, each scan consisting of application of corrections on a smaller lateral scale than during the previous scan. The analysis system is very flexible, and has been used to analyze many different types of variables. An example of horizontal divergence computed from a direct wind analysis is shown.

The terminal velocities for distilled water droplets falling through stagnant air are accurately determined. More than 1500 droplets of mass from 0.2 to 100,000 micrograms, embracing droplets so small that Stokes' law is obeyed up to and including droplets so large that they are mechanically unstable, were measured by a new method employing electronic techniques. An apparatus for the production of electrically charged artificial water droplets at a controllable rate is described. The over-all accuracy of the mass-terminal-velocity measurements is better than 0.7 per cent.

Results are presented from the second phase of the multiinstitution North American Land Data Assimilation System (NLDAS‐2) research partnership. In NLDAS, the Noah, Variable Infiltration Capacity, Sacramento Soil Moisture Accounting, and Mosaic land surface models (LSMs) are executed over the conterminous U.S. (CONUS) in realtime and retrospective modes. These runs support the drought analysis, monitoring and forecasting activities of the National Integrated Drought Information System, as well as efforts to monitor large‐scale floods. NLDAS‐2 builds upon the framework of the first phase of NLDAS (NLDAS‐1) by increasing the accuracy and consistency of the surface forcing data, upgrading the land surface model code and parameters, and extending the study from a 3‐year (1997–1999) to a 30‐year (1979–2008) time window. As the first of two parts, this paper details the configuration of NLDAS‐2, describes the upgrades to the forcing, parameters, and code of the four LSMs, and explores overall model‐to‐model comparisons of land surface water and energy flux and state variables over the CONUS. Focusing on model output rather than on observations, this study seeks to highlight the similarities and differences between models, and to assess changes in output from that seen in NLDAS‐1. The second part of the two‐part article focuses on the validation of model‐simulated streamflow and evaporation against observations. The results depict a higher level of agreement among the four models over much of the CONUS than was found in the first phase of NLDAS. This is due, in part, to recent improvements in the parameters, code, and forcing of the NLDAS‐2 LSMs that were initiated following NLDAS‐1. However, large inter‐model differences still exist in the northeast, Lake Superior, and western mountainous regions of the CONUS, which are associated with cold season processes. In addition, variations in the representation of sub‐surface hydrology in the four LSMs lead to large differences in modeled evaporation and subsurface runoff. These issues are important targets for future research by the land surface modeling community. Finally, improvement from NLDAS‐1 to NLDAS‐2 is summarized by comparing the streamflow measured from U.S. Geological Survey stream gauges with that simulated by four NLDAS models over 961 small basins.

Results are presented from the multi‐institution partnership to develop a real‐time and retrospective North American Land Data Assimilation System (NLDAS). NLDAS consists of (1) four land models executing in parallel in uncoupled mode, (2) common hourly surface forcing, and (3) common streamflow routing: all using a 1/8° grid over the continental United States. The initiative is largely sponsored by the Global Energy and Water Cycle Experiment (GEWEX) Continental‐Scale International Project (GCIP). As the overview for nine NLDAS papers, this paper describes and evaluates the 3‐year NLDAS execution of 1 October 1996 to 30 September 1999, a period rich in observations for validation. The validation emphasizes (1) the land states, fluxes, and input forcing of four land models, (2) the application of new GCIP‐sponsored products, and (3) a multiscale approach. The validation includes (1) mesoscale observing networks of land surface forcing, fluxes, and states, (2) regional snowpack measurements, (3) daily streamflow measurements, and (4) satellite‐based retrievals of snow cover, land surface skin temperature (LST), and surface insolation. The results show substantial intermodel differences in surface evaporation and runoff (especially over nonsparse vegetation), soil moisture storage, snowpack, and LST. Owing to surprisingly large intermodel differences in aerodynamic conductance, intermodel differences in midday summer LST were unlike those expected from the intermodel differences in Bowen ratio. Last, anticipating future assimilation of LST, an NLDAS effort unique to this overview paper assesses geostationary‐satellite‐derived LST, determines the latter to be of good quality, and applies the latter to validate modeled LST.

We tested four land surface parameterization schemes against long‐term (5 months) area‐averaged observations over the 15 km × 15 km First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) area. This approach proved to be very beneficial to understanding the performance and limitations of different land surface models. These four surface models, embodying different complexities of the evaporation/hydrology treatment, included the traditional simple bucket model, the simple water balance (SWB) model, the Oregon State University (OSU) model, and the simplified Simple Biosphere (SSiB) model. The bucket model overestimated the evaporation during wet periods, and this resulted in unrealistically large negative sensible heat fluxes. The SWB model, despite its simple evaporation formulation, simulated well the evaporation during wet periods, but it tended to underestimate the evaporation during dry periods. Overall, the OSU model ably simulated the observed seasonal and diurnal variation in evaporation, soil moisture, sensible heat flux, and surface skin temperature. The more complex SSiB model performed similarly to the OSU model. A range of sensitivity experiments showed that some complexity in the canopy resistance scheme is important in reducing both the overestimation of evaporation during wet periods and underestimation during dry periods. Properly parameterizing not only the effect of soil moisture stress but also other canopy resistance factors, such as the vapor pressure deficit stress, is critical for canopy resistance evaluation. An overly simple canopy resistance that includes only soil moisture stress is unable to simulate observed surface evaporation during dry periods. Given a modestly comprehensive time‐dependent canopy resistance treatment, a rather simple surface model such as the OSU model can provide good area‐averaged surface heat fluxes for mesoscale atmospheric models.

Model Output Statistics (MOS) is an objective weather forecasting technique which consists of determining a statistical relationship between a predictand and variables forecast by a numerical model at some projection time(s). It is, in effect, the determination of the “weather related” statistics of a numerical model. This technique, together with screening regression, has been applied to the prediction of surface wind, probability of precipitation, maximum temperature, cloud amount, and conditional probability of frozen precipitation. Predictors used include surface observations at initial time and predictions from the Subsynoptic Advection Model (SAM) and the Primitive Equation model used operationally by the National Weather Service. Verification scores have been computed, and, where possible, compared to scores for forecasts from other objective techniques and for the official forecasts. MOS forecasts of surface wind, probability of precipitation, and conditional probability of frozen precipitation are being disseminated by the National Weather Service over teletype and facsimile. It is concluded that MOS is a useful technique in objective weather forecasting.

The instruments on the Tropical Rainfall Measuring Mission (TRMM) satellite have been observing storms as well as rainfall since December 1997. This paper shows the results of a systematic search through seven full years of the TRMM database to find indicators of uncommonly intense storms. These include strong (> 40 dBZ) radar echoes extending to great heights, high lightning flash rates, and very low brightness temperatures at 37 and 85 GHz. These are used as proxy variables, indicating powerful convective updrafts. The main physical principles supporting this assertion involve the effects of such updrafts in producing and lofting large ice particles high into the storm, where TRMM's radar easily detects them near storm top. TRMM's passive microwave radiometer detects the large integrated ice water path as very low brightness temperatures, while high lightning flash rates are a physically related but instrumentally independent indicator. The geographical locations of these very intense convective storms demonstrate strong regional preferences for certain land areas while they are extremely rare over tropical oceans. Favored locations include the south-central United States, southeast South America, and equatorial Africa. Other regions have extreme storms mainly in specific seasons, such as the Sahel, the Indian subcontinent, and northern Australia. Because intense storms are distributed quite differently from rainfall, these maps provide some new metrics for global models, if they are to simulate the type of convection as a component of our climate system.

The new Version 2.3 of the GPCP Monthly analysis is described in terms of changes made to improve the homogeneity of the product, especially after 2002. These changes include corrections to cross calibration of satellite data inputs and updates to the gauge analysis. Over ocean, changes starting in 2003 result in an overall precipitation increase of 1.8% after 2009. Updating the gauge analysis to its final, high quality version increases the global land total by 1.8% for the post-2002 period. These changes correct a small, incorrect dip in the estimated global precipitation over the last decade in the earlier Version 2.2. The GPCP analysis is also used to describe global precipitation for 2017. The general La Nina pattern for 2017 is noted and the evolution from the early 2016 El Nino pattern is described. The 2017 global value is one of the highest for the 19792017 period, exceeded only by 2016 and 1998 (both El Nino years) and reinforces the small positive trend. Results for 2017 also reinforce significant trends in precipitation intensity (on a monthly scale) in the tropics. These results for 2017 indicate the value of the GPCP analysis for climate monitoring in addition to research.

A detailed description of the operational WSR-88D rainfall estimation algorithm is presented. This algorithm, called the Precipitation Processing System, produces radar-derived rainfall products in real time for forecasters in support of the National Weather Service’s warning and forecast missions. It transforms reflectivity factor measurements into rainfall accumulations and incorporates rain gauge data to improve the radar estimates. The products are used as guidance to issue flood watches and warnings to the public and as input into numerical hydrologic and atmospheric models. The processing steps to quality control and compute the rainfall estimates are described, and the current deficiencies and future plans for improvement are discussed.

article Free Access Share on On the Numerical Solution of Fredholm Integral Equations of the First Kind by the Inversion of the Linear System Produced by Quadrature Author: S. Twomey U. S. Weather Bureau, Washington, D. C. U. S. Weather Bureau, Washington, D. C.View Profile Authors Info & Claims Journal of the ACMVolume 10Issue 1Jan. 1963 pp 97–101https://doi.org/10.1145/321150.321157Published:01 January 1963Publication History 721citation2,290DownloadsMetricsTotal Citations721Total Downloads2,290Last 12 Months190Last 6 weeks25 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Detection of long‐term, linear trends is affected by a number of factors, including the size of trend to be detected, the time span of available data, and the magnitude of variability and autocorrelation of the noise in the data. The number of years of data necessary to detect a trend is strongly dependent on, and increases with, the magnitude of variance (σ N 2 ) and autocorrelation coefficient (ϕ) of the noise. For a typical range of values of σ N 2 and ϕ the number of years of data needed to detect a trend of 5%/decade can vary from ∼10 to >20 years, implying that in choosing sites to detect trends some locations are likely to be more efficient and cost‐effective than others. Additionally, some environmental variables allow for an earlier detection of trends than other variables because of their low variability and autocorrelation. The detection of trends can be confounded when sudden changes occur in the data, such as when an instrument is changed or a volcano erupts. Sudden level shifts in data sets, whether due to artificial sources, such as changes in instrumentation or site location, or natural sources, such as volcanic eruptions or local changes to the environment, can strongly impact the number of years necessary to detect a given trend, increasing the number of years by as much as 50% or more. This paper provides formulae for estimating the number of years necessary to detect trends, along with the estimates of the impact of interventions on trend detection. The uncertainty associated with these estimates is also explored. The results presented are relevant for a variety of practical decisions in managing a monitoring station, such as whether to move an instrument, change monitoring protocols in the middle of a long‐term monitoring program, or try to reduce uncertainty in the measurements by improved calibration techniques. The results are also useful for establishing reasonable expectations for trend detection and can be helpful in selecting sites and environmental variables for the detection of trends. An important implication of these results is that it will take several decades of high‐quality data to detect the trends likely to occur in nature.

The single most prominent signal in year-to-year climate variability is the Southern Oscillation, which is associated with fluctuations in atmospheric pressure at sea level in the tropics, monsoon rainfall, and wintertime circulation over North America and other parts of the extratropics. Although meteorologists have known about the Southern Oscillation for more than a half-century, its relation to the oceanic El Niño phenomenon was not recognized until the late 1960's, and a theoretical understanding of these relations has begun to emerge only during the past few years. The past 18 months have been characterized by what is probably the most pronounced and certainly the best-documented El Niño/Southern Oscillation episode of the past century. In this review meteorological aspects of the time history of the 1982-1983 episode are described and compared with a composite based on six previous events between 1950 and 1975, and the impact of these new observations on theoretical interpretations of the event is discussed.

A form of the critical success index (CSI) is used by the National Weather Service to indicate the value of warnings. This verification statistic assumes that the times when an event was neither expected nor observed are of no consequence. It can be shown that the CSI is not an unbiased indicator of forecast skill but is proportional to the frequency of the event being forecast. This innate bias is demonstrated theoretically and via example. An unbiased verification statistic appropriate for forecast of rare events is presented and applied to severe convective weather warnings. Comparisons of this score to the CSI show the extent of the penalty the CSI extracts from forecasters who work in areas that are not climatically prone to given events.

A power-spectrum analysis of horizontal wind speed is made over a wide range of frequencies by piecing together various portions of the spectrum. There appear to be two major eddy-energy peaks in the spectrum ; one peak occurs at a period of about 4 days, and a second peak occurs at a period of about 1 minute. Between the two peaks, a broad spectral gap is centered at a frequency ranging from 1 to 10 cycles per hour. The spectral gap seems to exist under varying terrain and synoptic conditions.

The states of thermal equilibrium (incorporating an adjustment of super-adiabatic stratification) as well as that of pure radiative equilibrium of the atmosphere are computed as the asymptotic steady state approached in an initial value problem. Recent measurements of absorptivities obtained for a wide range of pressure are used, and the scheme of computation is sufficiently general to include the effect of several layers of clouds. The atmosphere in thermal equilibrium has an isothermal lower stratosphere and an inversion in the upper stratosphere which are features observed in middle latitudes. The role of various gaseous absorbers (i.e., water vapor, carbon dioxide, and ozone), as well as the role of the clouds, is investigated by computing thermal equilibrium with and without one or two of these elements. The existence of ozone has very little effect on the equilibrium temperature of the earth's surface but a very important effect on the temperature throughout the stratosphere; the absorption of solar radiation by ozone in the upper and middle stratosphere, in addition to maintaining the warm temperature in that region, appears also to be necessary for the maintenance of the isothermal layer or slight inversion just above the tropopause. The thermal equilibrium state in the absence of solar insulation is computed by setting the temperature of the earth's surface at the observed polar value. In this case, the stratospheric temperature decreases monotonically with increasing altitude, whereas the corresponding state of pure radiative equilibrium has an inversion just above the level of the tropopause. A series of thermal equilibriums is computed for the distributions of absorbers typical of different latitudes. According to these results, the latitudinal variation of the distributions of ozone and water vapor may be partly responsible for the latitudinal variation of the thickness of the isothermal part of the stratosphere. Finally, the state of local radiative equilibrium of the stratosphere overlying a troposphere with the observed distribution of temperature is computed for each season and latitude. In the upper stratosphere of the winter hemisphere, a large latitudinal temperature gradient appears at the latitude of the polar-night jet stream, while in the upper statosphere of the summer hemisphere, the equilibrium temperature varies little with latitude. These features are consistent with the observed atmosphere. However, the computations predict an extremely cold polar night temperature in the upper stratosphere and a latitudinal decrease (toward the cold pole) of equilibrium temperature in the middle or lower stratosphere for winter and fall. This disagrees with observation, and suggests that explicit introduction of the dynamics of large scale motion is necessary.

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The general properties of the gamma distribution, which has several applications in meteorology, are discussed. A short review of the general properties of good statistical estimators is given. This is applied to the gamma distribution to show that the maximum likelihood estimators are jointly sufficient. A new, simple approximation of the likelihood solutions is given, and the efficiency of the fitting procedure is computed.

A station observation‐based global land monthly mean surface air temperature dataset at 0.5 × 0.5 latitude‐longitude resolution for the period from 1948 to the present was developed recently at the Climate Prediction Center, National Centers for Environmental Prediction. This data set is different from some existing surface air temperature data sets in: (1) using a combination of two large individual data sets of station observations collected from the Global Historical Climatology Network version 2 and the Climate Anomaly Monitoring System (GHCN + CAMS), so it can be regularly updated in near real time with plenty of stations and (2) some unique interpolation methods, such as the anomaly interpolation approach with spatially‐temporally varying temperature lapse rates derived from the observation‐based Reanalysis for topographic adjustment. When compared with several existing observation‐based land surface air temperature data sets, the preliminary results show that the quality of this new GHCN + CAMS land surface air temperature analysis is reasonably good and the new data set can capture most common temporal‐spatial features in the observed climatology and anomaly fields over both regional and global domains. The study also reveals that there are clear biases between the observed surface air temperature and the existing Reanalysis data sets, and they vary in space and seasons. Therefore the Reanalysis 2 m temperature data sets may not be suitable for model forcing and validation. The GHCN + CAMS data set will be mainly used as one of land surface meteorological forcing inputs to derive other land surface variables, such as soil moisture, evaporation, surface runoff, snow accumulation and snow melt, etc. As a byproduct, this monthly mean surface air temperature data set can also be applied to monitor surface air temperature variations over global land routinely or to verify the performance of model simulation and prediction.