Dew Point Depression Research Articles

Microphysics of fog bursting in polluted urban air

The goal of this work is to study sudden fog visibility (Vis) changes called fog bursting occurred during polluted urban air conditions. Fog bursting refers to a sudden drop of fog Vis below 500 m over 30 min. A comprehensive field observational campaign called Costal City Fog Microphysical Research (CCFMR) was carried out over the city of Tianjin, China during winter of 2016–2017. The six fog bursting cases occurred during the project suggest that droplet number concentration (Nd) and liquid water content (LWC) were higher than these of other conditions. The mass concentration of PM2.5 (NPM2.5) was also higher before the fog bursting events. The fog droplet spectra (FDS) followed a Junge distribution. During fog bursting events, Vis, surface air temperature (Ta) and dew point depression (Ta-Td) exhibited decreasing trends. On the other hand, mean and turbulence kinetic energy (MKE and TKE), as well as friction velocity (u∗) had increasing trends in time. After the fog bursting, environment stability changed from the neutral stage to weakly unstable stage, and fog became deeper and optically thicker. The FDS broadened drastically between 20 and 35 μm diameter, and smaller Nd increased up to 1 order of magnitude. It is concluded that fog bursting is positively affected by combination of decreasing Ta by radiative cooling, increasing RH, turbulence mixing, and high value of NPM2.5. Results from this work suggest that fog bursting conditions need to be studied in detail by numerical weather forecast (NWP) models that currently lack of these conditions.

A rule-based method for diagnosing radiation fog in an arid region from NWP forecasts

A reliable fog forecast can assist in water resource management and mitigate against hazardous effects on transport networks. However, diagnosing fog from numerical weather prediction models remains a challenge and various methods have been developed to achieve this. One approach is to use a multi rule-based method, where threshold values of multiple variables are employed to define fog conditions in the forecast. Such a method is presented here for next day forecasts of radiation fog at Abu Dhabi in the United Arab Emirates. Variables tested include the dew point depression, wind speed, modified Richardson index, inversion depth, relative humidity and liquid water content. Of these, the best performing rule included relative humidity (≥94%), wind speed (≤3ms−1) and inversion depth (>250 m) with a probability of detection (POD) (false alarm ratio (FAR)) of 0.62 (0.37). The worst performing rule was liquid water content with a POD (FAR) of 0.37 (0.31). Model performance was more sensitive to inversion depth than wind speed, and rules that included inversion depth performed better than rules that included the modified Richardson number. A qualitative spatial analysis showed that rules produced realistic fog patches when compared to satellite images. This suggests that the geophysical parameters chosen to define rules are well suited for defining fog as long as the model bias is sufficiently small to fall within the threshold values. This method worked well for the study region and did not demonstrate any seasonal bias.

Relationship of extreme precipitation, surface air temperature, and dew point temperature across the Tibetan Plateau

Global warming is expected to have profound socio-economic and environmental consequences, and one of the key concerns is extreme precipitation. The classic theory indicates that the variation of extreme precipitation will follow the thermodynamically based Clausius-Clapeyron relation (increasing at a rate of approximately 7%/°C). However, the interaction and the seasonal variation of the relationship between extreme precipitation and temperature has not been thoroughly integrated. Here, we use quantile regression and the binning method to process meteorological station data and the reanalysis data from 78 stations on the Tibetan Plateau (TP), and estimate the sensitivity and dependency of extreme precipitation to both surface air temperature and dew point temperature. The results indicate that the majority of the meteorological stations experience a positive scaling between extreme precipitation and surface air temperature, with the median of surface air temperature scaling close to 2.5%/°C. With regard to scaling during individual seasons, 80% of stations possess negative scaling of the 99th percentile of extreme precipitation with temperature in summer, while 63 stations (81% of the total) display positive trends in winter, and the other 19% have negative scaling. A stronger scaling (3.5%/°C) occurs between dewpoint temperature and extreme precipitation. For surface air temperature 8 °C, relative humidity decreases with increasing temperature and dewpoint depression increases, so dewpoint temperature increases more slowly than temperature, resulting in the stronger scaling relationship with dewpoint temperature. The depression of dewpoint temperature presents a slight decrease in −3–8 °C interval, but dewpoint temperature increases faster than other intervals, and the scaling relationship of dewpoint temperature is consistently larger than with surface air temperature due to the small increasing rate with dewpoint temperature and increasing extreme precipitation intensity. Our results emphasize that the increasing temperature has clear scaling and seasonal relationship with extreme precipitation on the TP, and the atmospheric humidity variation has an effect on the overall difference in the scaling relationships of extreme precipitation with surface air temperature and dewpoint temperature. The variations of seasonality and the effects of atmospheric humidity for extreme precipitation should also be carefully considered in future research.

Outdoor performance of organic photovoltaics at two different locations: A comparison of degradation and the effect of condensation

Tests on organic photovoltaics (OPV) mini modules, fabricated through a R2R process, in air and without hazardous solvents have been conducted in order to compare their outdoor performance, in Belo Horizonte, Brazil, and Bangor, North Wales, and assess the impact of the latitude and climate of the installation on the power generation and modules' lifetime. The test showed different profiles of degradation for each region and formulation, with a surprisingly faster degradation in Bangor. One of the possible sources of the increased degradation is the greater levels of condensation observed in Bangor. To verify the impact of condensation on the module stability, indoor tests have been conducted to relate the dew point depression to module degradation times. The results show that condensation is a significant stress factor in OPV and should be considered more prominently in reliability studies.

Main Factors Influencing Winter Visibility at the Xinjin Flight College of the Civil Aviation Flight University of China

Utilizing routine hourly meteorological data of Xinjin Airport and daily average PM2.5 concentration data for Chengdu, winter visibility characteristics at Xinjin Airport between 2013 and 2017 and their relationship with meteorological conditions and particulate matter were analyzed. Between 2013 and 2017, the average winter visibility in Xinjin Airport was lowest in January, followed by that in December. The occurrence frequency of haze days in winter was much higher than that of nonhaze (clean) days, being 90.2% and 9.8%, respectively. These were mainly mild haze days, with an occurrence frequency of 44.4%, while severe haze days occurred the least, with a frequency of 7.7%. The linear and nonlinear relationships between winter visibility, meteorological factors, and PM2.5 were measured using daily data in winter from 2013 to 2016. The linear correlation between PM2.5 concentration and visibility was the most evident, followed by that of relative humidity. Visibility had a higher nonlinear correlation with PM2.5 concentration, relative humidity, and dew point depression. When relative humidity was between 70% and 80%, the negative correlation between visibility and PM2.5 concentration was the most significant and could be described by a power function. The multivariate linear regression equation of PM2.5 concentration and relative humidity could account for 65.9% of the variation in winter visibility, and the multivariate nonlinear regression equation of PM2.5 concentration, relative humidity, and wind speed could account for 68.1% of the variation in winter visibility. These two equations reasonably represented the variation in winter visibility in 2017.

Surface Wetness as an Unexpected Control on Forest Exchange of Volatile Organic Acids

AbstractWe report bidirectional exchange of volatile acids, including isocyanic and alkanoic acids, over a pine forest across multiple seasons. The exchange velocity of these acids is well correlated with dew point depression, suggesting an equilibrium‐driven continuum of flux. Wetness on forest surfaces impacts the vertical exchange of gases, and we suggest that water films and droplets drive equilibrium partitioning, with acids being solvated in surface wetness and released through evaporation. Despite their volatility, these acids partition into neutral‐to‐alkaline aqueous films, consistent with reported dew pH. This relationship between exchange velocity and dew point depression holds for a wetter mixed forest, but not a very dry orchard. Dew point depression is an excellent indicator of acid fluxes so long as the canopy is occasionally wetted.

Prediction of dehydration performance of supersonic separator based on a multi-fluid model with heterogeneous condensation

Supersonic separation is a novel technology. A multi-fluid slip model for swirling flow with homogenous/heterogenous condensation and evaporation processes in the supersonic separator was built to estimate the separation efficiency. This model solves the governing equations of compressible turbulent gas phase and dispersed homogenous/heterogenous liquid phase considering droplet coalescence and interphase force. Its prediction accuracy for condensation and swirling flows was validated. Then, the flow field, slip velocity and droplet trajectory inside the separators with different swirl strengths were investigated. The maximum values of radial slip velocity are 29.2 and 8.26 m/s for inlet foreign droplet radius of 1.0 and 0.4 µm. It means the larger foreign droplet has a better condensation rate. However, the residence time of larger foreign droplet in core flow is shorten. Thus, the inlet radius of foreign droplet has to be moderate for best separation efficiency. Finally, the dehydration performances of separator were evaluated. The optimal radius of inlet foreign droplet to maximize the dehumidification and efficiency was found. For the separator with swirl strength of 22%, the optimal radius is 0.85 µm at inlet pressure of 250 kPa, where the maximum dew point depression is 42.41 °C and the water removal rate is 87.82%.

Short-duration precipitation extremes over Canada in a warmer climate

Short-duration precipitation extremes are widely used in the design of engineering infrastructure systems and they also lead to high impact flash flood events and landslides. Better understanding of these events in a changing climate is therefore critical. This study assesses characteristics of short-duration precipitation extremes of 1-, 3-, 6- and 12-h durations in terms of the precipitation-temperature (P–T) relationship in current and future climates for ten Canadian climatic regions using the limited area version of the global environment multiscale (GEM) model. The GEM simulations, driven by ERA-Interim reanalysis and two coupled global climate models (CanESM2 and MPI-ESM), reproduce the general observed regional P–T relationship characteristics in current climate (1981–2010), such as sub-CC (Clausius–Clapeyron) and CC scalings for the coastal and northern, and inland regions, respectively, albeit with some underestimation. Analysis of the transient climate change simulations suggests important shifts and/or extensions of the P–T curve to higher temperature bins in future climate (2071–2100) for RCP4.5 and 8.5 scenarios, particularly for 1-h duration. Analysis of the spatial patterns of dew point depression (temperature minus dew point temperature) and convective available potential energy (CAPE) corresponding to short-duration precipitation extremes for different temperature bins show their changing relative importance from low to high temperature bins. For the low-temperature bins, short-duration precipitation extremes are largely due to high relative humidity, while for high-temperature bins, strong convection due to atmospheric instability brought by surface warming is largely responsible. The analysis thus addresses some of the key knowledge gaps related to the behavior of P–T relationship and associated mechanisms for the Canadian regions.

Observed and model‐simulated thermodynamic processes associated with urban heavy rainfall events over Bangalore, India

AbstractA total of 32 rainfall events spanning the period from 2012 to 2014 over the urban Indian city of Bangalore were simulated using the Weather Research and Forecast (WRF) model. Model simulations were carried out with a four‐nested domain initialized with Global Forecast System (GFS) data and the forecast was generated on an hourly basis. The forecasted rainfall at hobli level (Bangalore has 34 hobli divisions with an area of each hobli of the order of about 10 km2) was evaluated in terms of its intensity and pattern of spatial distribution by comparing it with corresponding rain‐gauge observations. Also, the rainfall forecast skill of the model was evaluated statistically by computing root mean square error, bias and mean absolute error. Thermodynamic variables such as equivalent potential temperature, convective available potential energy (CAPE), convective inhibition (CIN), K index (KI), lifted index (LI) and total totals index (TTI) were also derived from the simulated model parameters for all the events and then verified against corresponding observations. The results showed that the WRF model could simulate the rainfall events and associated thermodynamic features qualitatively; however, there were few hoblis where the relative errors in the forecast were > 100%. The forecast errors were relatively lower for cases during the southwest monsoon season compared with other seasons. It was found that the model underestimated thermodynamic indices such as CAPE, dew point depression and the simulated LI were positive; these were indicative of the model's limitation in simulating intense convection and a possible reason for underpredicted rainfall simulations.

Impact of Weak Coupling between Land and Atmosphere Data Assimilation Systems on Environment and Climate Change Canada’s Global Deterministic Prediction System

Abstract A new ensemble-based land surface data assimilation (DA) system is coupled with the atmospheric four-dimensional ensemble-variational data assimilation (4D-EnVar) system with the goal of improving the analyses within Environment and Climate Change Canada’s Global Deterministic Prediction System. Since 2001, the sequential assimilation of surface variables is used to generate the initial conditions to launch the Global Environmental Multiscale (GEM) coupled forecast model. The work presented here is to replace the sequential DA with an independent surface DA system, the Canadian Land Data Assimilation System (CaLDAS) assimilating screen-level observations, and to compare assimilation experiments with CaLDAS run in uncoupled and weakly coupled modes. In the uncoupled mode, CaLDAS is used to initialize the forecast without interacting with the 4D-EnVar system. In the coupled mode, the analyses generated from CaLDAS and 4D-EnVar are used to initialize the forecast model. The analyses and forecasts from uncoupled and coupled runs are evaluated against surface and radiosonde observations over different subdomains to conclude the impact of coupling CaLDAS with 4D-EnVar. Results indicate a statistically significant reduction in bias and standard deviation at the surface for screen-level temperature and dewpoint temperature on the order of 0.1 K, and in the lower troposphere between 1000 and 500 hPa on the order of 0.1 dam for geopotential height and 0.1 K for air temperature and dewpoint depression in the coupled DA runs. The positive impact persists up to 5 days over some subdomains. It is concluded that the coupled DA approach generally performs better than the uncoupled version.

Climate Change Increases the Potential for Extreme Wildfires

AbstractPyrocumulonimbus (pyroCb) wildfires cause devastation in many regions globally. Given that fire‐atmosphere coupling is associated with pyroCbs, future changes in coincident high index values of atmospheric instability and dryness (C‐Haines) and near‐surface fire weather are assessed for southeastern Australia using a regional climate projection ensemble. We show that observed pyroCb events occur predominantly on forested, rugged landscapes during extreme C‐Haines conditions, but over a wide range of surface fire weather conditions. Statistically significant increases in the number of days where both C‐Haines and near‐surface fire weather values are conducive to pyroCb development are projected across southeastern Australia, predominantly for November (spring), and less strongly for December (summer) in 2060‐2079 versus 1990‐2009, with future C‐Haines increases linked to increased 850‐hPa dewpoint depression. The increased future occurrence of conditions conducive to pyroCb development and their extension into spring have implications for mitigating these dangerous wildfires and urbanizing fire‐prone landscapes.

Optimization of triethylene glycol dehydration of natural gas

Dehydration using triethylene glycol (TEG) is widely used in natural gas processing to avoid corrosion and plugging of the flow lines. Optimization using Aspen HYSYS simulator and optimizer tool was performed to minimize the processing cost considering different sets of parameters: TEG circulation rate, numbers of theoretical trays (in the absorber and the stripping gas column), feed gas pressure and temperature, gas flow rate, gas price level and stripping gas rate. In addition to typical dew point depression simulation results reported in other publications, the present investigation also provides the minimum processing cost including both utilities and capital cost. The results based on the parametric optimization study yield criteria for design and optimum operating conditions for the dehydration process.

Low-visibility forecasts for different flight planning horizons using tree-based boosting models

Abstract. Low-visibility conditions enforce special procedures that reduce the operational flight capacity at airports. Accurate and probabilistic forecasts of these capacity-reducing low-visibility procedure (lvp) states help the air traffic management in optimizing flight planning and regulation. In this paper, we investigate nowcasts, medium-range forecasts, and the predictability limit of the lvp states at Vienna International Airport. The forecasts are generated with boosting trees, which outperform persistence, climatology, direct output of numerical weather prediction (NWP) models, and ordered logistic regression. The boosting trees consist of an ensemble of decision trees grown iteratively on information from previous trees. Their input is observations at Vienna International Airport as well as output of a high resolution and an ensemble NWP model. Observations have the highest impact for nowcasts up to a lead time of +2 h. Afterwards, a mix of observations and NWP forecast variables generates the most accurate predictions. With lead times longer than +7 h, NWP output dominates until the predictability limit is reached at +12 d. For lead times longer than +2 d, output from an ensemble of NWP models improves the forecast more than using a deterministic but finer resolved NWP model. The most important predictors for lead times up to +18 h are observations of lvp and dew point depression as well as NWP dew point depression. At longer lead times, dew point depression and evaporation from the NWP models are most important.

Impact of Air Pollution Controls on Radiation Fog Frequency in the Central Valley of California

AbstractIn California's Central Valley, tule fog frequency increased 85% from 1930 to 1970, then declined 76% in the last 36 winters. Throughout these changes, fog frequency exhibited a consistent north‐south trend, with maxima in southern latitudes. We analyzed seven decades of meteorological data and five decades of air pollution data to determine the most likely drivers changing fog, including temperature, dew point depression, precipitation, wind speed, and NOx (oxides of nitrogen) concentration. Climate variables, most critically dew point depression, strongly influence the short‐term (annual) variability in fog frequency; however, the frequency of optimal conditions for fog formation show no observable trend from 1980 to 2016. NOx concentration, which has a decreasing north‐south concentration gradient, declined continuously over this period, consistent with the long‐term temporal and spatial trends in fog. As development in the Central Valley increased direct particle and other pollutant emissions from 1930 to 1970, fog frequency increased. Following the Clean Air Act, particle emissions quickly declined, and NOx emissions declined steadily, reducing the cloud condensation nuclei (CCN) available for fog formation. As a precursor of ammonium nitrate aerosols, which are efficient CCN, we used NOx measurements and emission trends as a proxy for the CCN trend. We conclude that while the short‐term fog variability is dominantly driven by climate fluctuations, the longer‐term temporal and spatial changes in fog have been driven by changes in air pollution. For conditions close to the dew point, a decrease in fog of 5 days/year per 10 parts per billion NOx decrease occurred across the Central Valley.

Implementation of a GNSS meteorological model to the estimation of the Haines Index

BackgroundThe objective of this study was to look for a replacement to the radiosonde measurements that are necessary for the construction of an index of potential wildfire severity (i.e., Haines Index, HI) in areas of South America that have had few to no radiosonde launches. To this end, we tested the application of GPT2w, an empirical model originally developed for Global Navigation Satellite System (GNSS) meteorology. By using the GPT2w model, and starting from measured surface meteorological data (air pressure, temperature, and relative humidity), estimators of air temperature and dew-point air temperature at different pressure levels were generated. The selected testing area was a region of South America that included most of the Río de la Plata drainage basin, along with two hazardous areas: Sierras de Córdoba in Argentina and Serra da Canastra in Brazil. This region was chosen due to the availability of the radiosonde launches required for comparison during 2016.ResultsTo characterize the regional performance of HI, we used data from the European Centre for Medium-Range Weather Forecast’s (ECMWF) reanalysis model (ERA Interim; Dee et al., Quarterly Journal of the Royal Meteorological Society 137: 553–597, 2011) for the period 2000 to 2016. A statistical analysis of the differences between the simulated HI from GPT2w (HI_GPT2w) and the HI values derived from radiosonde measurements (HI_R) was performed. The results showed that HI_GPT2w reproduced HI_R values about 50% of the time, most accurately for low-severity HI values (2 to 3, on a scale of 2 to 6). In general, HI_GPT2w exhibited an underestimation of HI that increased as the index value increased. We analyzed how this underestimation affected the different HI variants calculated. To this end, we recall that each HI variant results from the sum of the stability and moisture terms. The stability term resulted from the temperature difference at different pressure levels while the moisture term is represented by the dew-point depression. Thus, the pressure level limits in the stability term define the HI variant. In this study, we used the Low-variant HI (950 and 850 hPa) and the Mid-variant HI (850 and 700 hPa). If we analyze this underestimation according to the HI variants, the moisture term is responsible for the Low variant underestimation and the stability term is responsible for the Mid variant.ConclusionThe simple application of GPT2w to extrapolate the vertical values of temperature and its dew-point depression is not enough to reproduce the Haines Index as it was measured from radiosonde measurements. Nevertheless, an improved application of GPT2w and the extrapolated saturation water vapor pressure by using the Integrated Water Vapor from Global Navigation Satellite System (GNSS-IWV) values could improve the results.

Models for Estimating Precipitable Water Vapour and Variation of Dew Point Temperature with Other Parameters at Owerri, South Eastern, Nigeria

Precipitable water vapour (PWV) is a vital component of the atmosphere and appreciably controls many atmospheric processes. The PWV is not easy to measure with sufficient spatial and time resolution under all weather conditions. In this paper, three precipitable water vapour models; the Smith, Won and Leckner’s models were evaluated and compared for Owerri (Latitude 5.48°N, Longitude 7.00°E, and 91 m above sea level) using meteorological parameters of monthly average daily maximum temperature, minimum temperature and relative humidity during the period of sixteen years (2000-2015). The Leckner’s model was found most suitable and therefore recommended for estimating PWV for the location with range between 3.253 and 4.662 cm. The highest PWV occurred in June for Won and Leckner’s models while for Smith’s model it occurred in September; the lowest PWV occurred in January for all the evaluated models. The result showed that high values of dew point temperature (Tdew), PWV and relative humidity (RH) were observed during the raining season and low values in the dry season; this is an indication that the dew point temperature is a reflection of the PWV and RH. The dew point temperature is an opposite reflection of the virtual temperature (Tvirtual), potential temperature (Tpotential) and mean temperature (Tmean). The dew point temperature increases and decreases with mean temperature in the months from January to March and in July respectively for the location under investigation. The values of the dew point temperature indicated that the air is stable signifying no development of severe weather condition like thunderstorms. The maximum and minimum virtual temperature correction of 3.3246°C and 2.3371°C occurred in June and January respectively while for the dew point depression, it occurred in the months of January and September with 8.7514°C and 2.1094°C. The descriptive statistical analysis shows that the dew point temperature, potential temperature, mean temperature and virtual temperature correction data spread out more to the left of their mean value (negatively skewed), while the virtual temperature and dew point depression data spread out more to the right of their mean value (positively skewed). The dew point temperature and the virtual temperature correction data have positive kurtosis which indicates a relatively peaked distribution and possibility of a leptokurtic distribution while the virtual temperature, potential temperature, mean temperature and dew point depression data have negative kurtosis which indicates a relatively flat distribution and possibility of platykurtic distribution.

Moisture Sorption Models for Fuel Beds of Standing Dead Grass in Alaska

Sorption models were developed to predict the moisture content in fuelbeds of standing dead grass from ambient weather measurements. Intuition suggests that the response time of standing dead grass to diurnal changes in weather is negligible and that moisture content tracks the equilibrium moisture content under most field conditions. This assumption suggests that moisture content could be modelled by empirically fitting coefficients to equations of equilibrium moisture content using field measurements. Here, six equations commonly used in wildland fire management and other industries were fit using 293 measurements of weather and moisture content in standing dead grass from Alaska, U.S.A. Predictors were air temperature and either relative humidity or dewpoint depression. Mean absolute errors of the best three models were approximately 1.16% of moisture content. The models predicted well the moisture content of an independently collected dataset from Canada but less so a set from Australia. The models may be used in wildland fire danger rating and fire behavior prediction systems.

An analytical model for predicting frosting limit in membranes

Membrane energy exchangers have been used in the HVAC industry recently in order to reduce energy consumption and reduce the risk of frost formation. In this paper, frosting in membranes is studied with an analytical model based on thermal and mass resistances to determine the temperature and relative humidity at the surface of a membrane at steady state. The model is verified with experimental and numerical data. The main goal of this paper is to investigate the effects of design and operating parameters on the onset of saturation in order to prevent or delay frosting in a membrane. The maximum cold temperature which leads to frost formation on a surface at a specific air relative humidity is defined as the frosting limit. The frosting limit for a membrane is presented for different temperature and humidity conditions. A new parameter, allowable dew point depression of the surface (ΔDP), is defined as the difference between the dew point temperature of the air stream and the temperature at the top surface of the membrane. A sensitivity study of ΔDP demonstrates that the water vapor permeability of the membrane, heat transfer coefficient, and thickness of the membrane impact the frosting limit. The main conclusion is that increasing the moisture transfer rate through the membrane decreases the frosting limit.

Exploring seasonal and regional relationships between the Evaporative Stress Index and surface weather and soil moisture anomalies across the United States

Abstract. This study uses correlation analyses to explore relationships between the satellite-derived Evaporative Stress Index (ESI) – which depicts standardized anomalies in an actual to reference evapotranspiration (ET) fraction – and various land and atmospheric variables that impact ET. Correlations between the ESI and forcing variable anomalies calculated over sub-seasonal timescales were computed at weekly and monthly intervals during the growing season. Overall, the results revealed that the ESI is most strongly correlated to anomalies in soil moisture and 2 m dew point depression. Correlations between the ESI and precipitation were also large across most of the US; however, they were typically smaller than those associated with soil moisture and vapor pressure deficit. In contrast, correlations were much weaker for air temperature, wind speed, and radiation across most of the US, with the exception of the south-central US where correlations were large for all variables at some point during the growing season. Together, these results indicate that changes in soil moisture and near-surface atmospheric vapor pressure deficit are better predictors of the ESI than precipitation and air temperature anomalies are by themselves. Large regional and seasonal dependencies were also observed for each forcing variable. Each of the regional and seasonal correlation patterns were similar for ESI anomalies computed over 2-, 4-, and 8-week time periods; however, the maximum correlations increased as the ESI anomalies were computed over longer time periods and also shifted toward longer averaging periods for the forcing variables.

Forecasting Rapid Drought Intensification Using the Climate Forecast System (CFS)

AbstractIn this study, a statistical method is developed to generate probabilistic forecasts of U.S. Drought Monitor (USDM)‐depicted drought intensification over two‐, four‐, and six‐week time periods using recent observations and forecast model output from the Climate Forecasting System (CFS). The predictors used include weekly anomalies in precipitation, potential evapotranspiration, dew point depression, and soil moisture computed over different time lags. A comparison between the baseline skill obtained using recent observations only and the skill obtained by adding CFS forecast fields as predictors shows that the inclusion of CFS model output leads to only a very modest increase in skill (about 14% increase in variance explained over the central and eastern United States). An analysis of this result reveals that the small increase in skill is due to limited skill in the CFS forecasts themselves, rather than to a time delay in the USDM response to conditions on the ground. Perfect model experiments also show that not all forecast lead times are equally important. For example, in the upper Midwest and western United States, the first two weeks account for at least two thirds of the total realizable skill for a four‐week forecast.