Effects of Secular Changes in Frequency of Observations and Observational Errors on Monthly Mean MSLP Summary Statistics Derived from ICOADS

Abstract A Monte Carlo technique has been employed to assess how sextile mean sea level pressure (MSLP) statistics derived from ship observations can be affected by changes in the frequency of observations. The results show that when the number of observations is small (less than 20 per month), the estimates of the first sextile as well as the intersextile range, which is considered to be a resistant estimate of the standard deviation, can contain large biases. The results also suggest that, while changes in the frequency of observations do not have strong impacts on the standard way of estimating the standard deviation, such statistics are strongly affected by secular trends in observational error statistics. The results are applied to examine the increasing trend in cool season (December–March) Pacific cyclone activity during the second half of the twentieth century. The results show that the trends in sextile statistics derived from the NCEP–NCAR reanalysis data are only consistent with those derived from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) summary statistics if biases due to changes in the frequency of observation are not taken into account. When such biases are accounted for, the trends derived from the observations are significantly smaller than those derived from the reanalysis data. As for the increasing trend in MSLP variance, the trends derived from the ICOADS statistics are smaller than those derived from the reanalysis regardless of whether corrections are made to account for the secular trend in MSLP error statistics. In either case, the corrections that have to be applied have the same order of magnitude as the observed trends. The two main conclusions are that 1) climate statistics can be strongly affected by changes in frequency of observations as well as changes in observational error statistics and 2) the trends in North Pacific winter cyclone activity, as derived from NCEP–NCAR reanalysis data, appear to be significantly larger than similar trends computed from ICOADS sextile and variance statistics, when biases due to changes in frequency of observations and observational error statistics have been taken into account.

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Climatology and Changes of Extratropical Cyclone Activity: Comparison of ERA-40 with NCEP–NCAR Reanalysis for 1958–2001

Journal of Climate ◽

10.1175/jcli3781.1 ◽

2006 ◽

Vol 19 (13) ◽

pp. 3145-3166 ◽

Cited By ~ 174

Author(s):

Xiaolan L. Wang ◽

Val R. Swail ◽

Francis W. Zwiers

Keyword(s):

North Atlantic ◽

Storm Track ◽

Northern Europe ◽

Cyclone Activity ◽

Historical Trends ◽

Sea Level Pressure ◽

The North ◽

Abrupt Changes ◽

Increasing Trend ◽

Activity Comparison

Abstract In this study, a cyclone detection/tracking algorithm was used to identify cyclones from two gridded 6-hourly mean sea level pressure datasets: the 40-yr ECMWF Re-Analysis (ERA-40) and the NCEP–NCAR reanalysis (NNR) for 1958–2001. The cyclone activity climatology and changes inferred from the two reanalyses are intercompared. The cyclone climatologies and trends are found to be in reasonably good agreement with each other over northern Europe and eastern North America, while ERA-40 shows systematically stronger cyclone activity over the boreal extratropical oceans than does NNR. However, significant differences between ERA-40 and NNR are seen over the austral extratropics. In particular, ERA-40 shows significantly greater strong-cyclone activity and less weak-cyclone activity over all oceanic areas south of 40°S in all seasons, while it shows significantly stronger cyclone activity over most areas of the austral subtropics in the warm seasons. The most notable historical trends in cyclone activity are found to be associated with strong-cyclone activity. Over the boreal extratropics, both ERA-40 and NNR show a significant increasing trend in January–March (JFM) strong-cyclone activity over the high-latitude North Atlantic and over the midlatitude North Pacific, with a significant decreasing trend over the midlatitude North Atlantic and a small increasing trend over northern Europe. The JFM changes over the North Atlantic are associated with the mean position of the storm track shifting about 181 km northward. Importantly, there is no evidence of abrupt changes identified for the boreal extratropics, although previous studies have suggested that the upward trend found in the NNR data could be biased high. However, there exist a few abrupt changes over the austral extratropics, which appear to be attributable to the increasing availability of observations assimilated in the reanalyses. After diminishing the effects of these abrupt changes, strong-cyclone activity over the austral circumpolar oceanic region is identified to have an increasing trend in October–December (OND) and July–September (JAS), with a decreasing trend over the 40°–60°S zone in JAS.

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Complete recovery of values in Diophantine systems (CORVIDS)

10.31234/osf.io/7shr8 ◽

2018 ◽

Author(s):

Sean Wilner ◽

Katherine Wood ◽

Daniel J. Simons

Keyword(s):

Standard Deviation ◽

Open Source ◽

Diophantine Equations ◽

Source Code ◽

Complete Recovery ◽

Summary Statistics ◽

Data Set ◽

Raw Data ◽

Source Program ◽

User Friendly

Raw data are often unavailable, and all that may remain of a data set are its summary statistics. When these data are integers on a fixed scale, such as Likert-style ratings, and their mean, standard deviation, and sample size are known, it is possible to reconstruct every raw distribution that gives rise to those summary statistics using a system of Diophantine equations. We have developed the open-source program CORVIDS (COmplete Reconstruction of Values In Diophantine Systems) to deterministically reconstruct raw data from summary statistics using this technique. The solutions generated by the program are provably complete. Here we describe the implementation, provide examples and use cases, and prove the correctness of the underlying mathematics. CORVIDS is open-source and available as source code or as stand-alone, user-friendly applications for macOS and Windows.

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The influence of the enhancement of the convective activities of tropical Pacific on the trend of stratospheric sudden warmings in the period of 1948-2003

10.21203/rs.3.rs-390163/v1 ◽

2021 ◽

Author(s):

Yuanpu Li ◽

Zhiping Wen

Keyword(s):

Northern Hemisphere ◽

Planetary Wave ◽

Reanalysis Data ◽

Tropical Pacific ◽

High Latitudes ◽

Central Pacific ◽

Data Set ◽

Increasing Trend ◽

Environmental Prediction ◽

Hemisphere Winter

Abstract The exploration of the trend of stratospheric sudden warming (SSW) in the Northern Hemisphere is conducive to predict SSWs in the future. Utilizing the National Centre for Environmental Prediction (NCEP) (1948–2017) and Japanese 55-year reanalysis data (JRA55) (1958–2017), we investigated the duration and strength of SSWs in the Northern Hemisphere winter (December-February). We found the duration of SSWs has an increasing trend and the strength of SSWs tends to strengthen from 1948 to 2003. However, after 2003, these trends did not continue. We also utilize the observed cloudiness from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) to examine the convective activities in the tropical Pacific and found that the convective activities in the tropical central Pacific are enhanced during the period of 1948–2003, and the trend of the enhancement of the convective activities ceases after 2003. The circulation anomalies caused by the enhanced convective activities propagate to the troposphere at high latitudes through wave trains. The anomalies of circulation and the climatic circulation at high latitudes interfere with each other and superimpose, which has a significant impact on planetary wave 1 (PW1). As a result, the PW1 in the troposphere also showed an increasing trend from 1948 to 2003 and a decreasing trend after 2003. After the stratosphere filters out the planetary wave with a large wavenumber, PW1 accounts for more proportion of planetary waves, which causes the trend of SSW to change synchronously.

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Three variables are better than one: detection of european winter windstorms causing important damages

Natural Hazards and Earth System Science ◽

10.5194/nhess-14-981-2014 ◽

2014 ◽

Vol 14 (4) ◽

pp. 981-993 ◽

Cited By ~ 4

Author(s):

M.-S. Deroche ◽

M. Choux ◽

F. Codron ◽

P. Yiou

Keyword(s):

Sea Level ◽

Large Scale ◽

Insurance Industry ◽

Surface Wind ◽

Relative Vorticity ◽

Reanalysis Data ◽

Data Set ◽

Sea Level Pressure ◽

Level Pressure ◽

Wind Speeds

Abstract. In this paper, we present a new approach for detecting potentially damaging European winter windstorms from a multi-variable perspective. European winter windstorms being usually associated with extra-tropical cyclones (ETCs), there is a coupling between the intensity of the surface wind speeds and other meso-scale and large-scale features characteristic of ETCs. Here we focus on the relative vorticity at 850 hPa and the sea level pressure anomaly, which are also used in ETC detection studies, along with the ratio of the 10 m wind speed to its 98th percentile. When analysing 10 events known by the insurance industry to have caused extreme damages, we find that they share an intense signature in each of the 3 fields. This shows that the relative vorticity and the mean sea level pressure have a predictive value of the intensity of the generated windstorms. The 10 major events are not the most intense in any of the 3 variables considered separately, but we show that the combination of the 3 variables is an efficient way of extracting these events from a reanalysis data set.

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Technology Tips: Visual Representations of Mean and Standard Deviation

Mathematics Teacher ◽

10.5951/mt.89.8.0688 ◽

1996 ◽

Vol 89 (8) ◽

pp. 688-692

Author(s):

Charles Vonder Embse ◽

Arne Engebretsen

Keyword(s):

Standard Deviation ◽

Visual Representations ◽

Visual Display ◽

Graphing Calculators ◽

Statistical Information ◽

Summary Statistics ◽

Data Set ◽

The Mean ◽

Nonparametric Statistical ◽

Bar Graphs

Summary statistics used to describe a data set are some of the most commonly taught statistical concepts in the secondary curriculum. Mean, median, mode, range, and standard deviation are topics that can be found in nearly every program. Technology empowers us to access these concepts and easily to create visual displays that interpret and describe the data in ways that enhance students' understanding. Many graphing calculators allow students to display nonparametric statistical information using a box-and-whiskers plot or a modified box plot showing a visual representation of the median, upper and lower quartiles, and the range of the data. But how can students visually display the mean of the data or show what it means to be within one standard deviation of the mean? One way to create this type of visual display is with a bar graph and constant functions. Unfortunately, graphing calculators, and some computer programs, only display histograms and not bar graphs. The tips in this issue focus on using graphing calculators to draw bar graphs that can help students visualize and interpret the mean and standard deviation of a data set.

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Satellite-driven downscaling of global reanalysis precipitation products for hydrological applications

Hydrology and Earth System Sciences ◽

10.5194/hess-18-5077-2014 ◽

2014 ◽

Vol 18 (12) ◽

pp. 5077-5091 ◽

Cited By ~ 11

Author(s):

H. Seyyedi ◽

E. N. Anagnostou ◽

E. Beighley ◽

J. McCollum

Keyword(s):

Relative Error ◽

Stage Iv ◽

Reanalysis Data ◽

Precipitation Data ◽

Error Statistics ◽

Data Set ◽

Mean Relative Error ◽

Precipitation Product ◽

Basin Scale ◽

Global Reanalysis

Abstract. Deriving flood hazard maps for ungauged basins typically requires simulating a long record of annual maximum discharges. To improve this approach, precipitation from global reanalysis systems must be downscaled to a spatial and temporal resolution applicable for flood modeling. This study evaluates such downscaling and error correction approaches for improving hydrologic applications using a combination of NASA's Global Land Data Assimilation System (GLDAS) precipitation data set and a higher resolution multi-satellite precipitation product (TRMM). The study focuses on 437 flood-inducing storm events that occurred over a period of ten years (2002–2011) in the Susquehanna River basin located in the northeastern United States. A validation strategy was devised for assessing error metrics in rainfall and simulated runoff as function of basin area, storm severity, and season. The WSR-88D gauge-adjusted radar-rainfall (stage IV) product was used as the reference rainfall data set, while runoff simulations forced with the stage IV precipitation data set were considered as the runoff reference. Results show that the generated rainfall ensembles from the downscaled reanalysis product encapsulate the reference rainfall. The statistical analysis consists of frequency and quantile plots plus mean relative error and root-mean-square error statistics. The results demonstrated improvements in the precipitation and runoff simulation error statistics of the satellite-driven downscaled reanalysis data set compared to the original reanalysis precipitation product. Results vary by season and less by basin scale. In the fall season specifically, the downscaled product has 3 times lower mean relative error than the original product; this ratio increases to 4 times for the simulated runoff values. The proposed downscaling scheme is modular in design and can be applied on any gridded satellite and reanalysis data set.

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Subseasonal relationship between Arctic and Eurasian surface air temperature

Scientific Reports ◽

10.1038/s41598-021-83486-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hye-Jin Kim ◽

Seok-Woo Son ◽

Woosok Moon ◽

Jong-Seong Kug ◽

Jaeyoung Hwang

Keyword(s):

Air Temperature ◽

Diabatic Heating ◽

Surface Air Temperature ◽

Significant Negative Correlation ◽

Cold Season ◽

Reanalysis Data ◽

Necessary Condition ◽

Sea Level Pressure ◽

Blocking High ◽

Pressure Anomaly

AbstractThe subseasonal relationship between Arctic and Eurasian surface air temperature (SAT) is re-examined using reanalysis data. Consistent with previous studies, a significant negative correlation is observed in cold season from November to February, but with a local minimum in late December. This relationship is dominated not only by the warm Arctic-cold Eurasia (WACE) pattern, which becomes more frequent during the last two decades, but also by the cold Arctic-warm Eurasia (CAWE) pattern. The budget analyses reveal that both WACE and CAWE patterns are primarily driven by the temperature advection associated with sea level pressure anomaly over the Ural region, partly cancelled by the diabatic heating. It is further found that, although the anticyclonic anomaly of WACE pattern mostly represents the Ural blocking, about 20% of WACE cases are associated with non-blocking high pressure systems. This result indicates that the Ural blocking is not a necessary condition for the WACE pattern, highlighting the importance of transient weather systems in the subseasonal Arctic-Eurasian SAT co-variability.

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Statistically rigorous calculations do not support common input and long-term synchronization of motor-unit firings

Journal of Neurophysiology ◽

10.1152/jn.00725.2013 ◽

2014 ◽

Vol 112 (11) ◽

pp. 2729-2744 ◽

Cited By ~ 11

Author(s):

Carlo J. De Luca ◽

Joshua C. Kline

Keyword(s):

Motor Unit ◽

Statistical Tests ◽

Vastus Lateralis ◽

Common Input ◽

Data Set ◽

Order Of Magnitude ◽

Score Method ◽

Surface Electromyographic Signal ◽

Measure Synchronization

Over the past four decades, various methods have been implemented to measure synchronization of motor-unit firings. In this work, we provide evidence that prior reports of the existence of universal common inputs to all motoneurons and the presence of long-term synchronization are misleading, because they did not use sufficiently rigorous statistical tests to detect synchronization. We developed a statistically based method (SigMax) for computing synchronization and tested it with data from 17,736 motor-unit pairs containing 1,035,225 firing instances from the first dorsal interosseous and vastus lateralis muscles—a data set one order of magnitude greater than that reported in previous studies. Only firing data, obtained from surface electromyographic signal decomposition with >95% accuracy, were used in the study. The data were not subjectively selected in any manner. Because of the size of our data set and the statistical rigor inherent to SigMax, we have confidence that the synchronization values that we calculated provide an improved estimate of physiologically driven synchronization. Compared with three other commonly used techniques, ours revealed three types of discrepancies that result from failing to use sufficient statistical tests necessary to detect synchronization. 1) On average, the z-score method falsely detected synchronization at 16 separate latencies in each motor-unit pair. 2) The cumulative sum method missed one out of every four synchronization identifications found by SigMax. 3) The common input assumption method identified synchronization from 100% of motor-unit pairs studied. SigMax revealed that only 50% of motor-unit pairs actually manifested synchronization.

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MAX-DOAS observations of aerosols, formaldehyde and nitrogen dioxide in the Beijing area: comparison of two profile retrieval approaches

Atmospheric Measurement Techniques ◽

10.5194/amt-8-941-2015 ◽

2015 ◽

Vol 8 (2) ◽

pp. 941-963 ◽

Cited By ~ 47

Author(s):

T. Vlemmix ◽

F. Hendrick ◽

G. Pinardi ◽

I. De Smedt ◽

C. Fayt ◽

...

Keyword(s):

Standard Deviation ◽

A Priori ◽

Systematic Bias ◽

Aerosol Extinction ◽

Data Set ◽

Near Surface ◽

Beijing Area ◽

Least Squares Fit ◽

Relative Differences ◽

Good Agreement

Abstract. A 4-year data set of MAX-DOAS observations in the Beijing area (2008–2012) is analysed with a focus on NO2, HCHO and aerosols. Two very different retrieval methods are applied. Method A describes the tropospheric profile with 13 layers and makes use of the optimal estimation method. Method B uses 2–4 parameters to describe the tropospheric profile and an inversion based on a least-squares fit. For each constituent (NO2, HCHO and aerosols) the retrieval outcomes are compared in terms of tropospheric column densities, surface concentrations and "characteristic profile heights" (i.e. the height below which 75% of the vertically integrated tropospheric column density resides). We find best agreement between the two methods for tropospheric NO2 column densities, with a standard deviation of relative differences below 10%, a correlation of 0.99 and a linear regression with a slope of 1.03. For tropospheric HCHO column densities we find a similar slope, but also a systematic bias of almost 10% which is likely related to differences in profile height. Aerosol optical depths (AODs) retrieved with method B are 20% high compared to method A. They are more in agreement with AERONET measurements, which are on average only 5% lower, however with considerable relative differences (standard deviation ~ 25%). With respect to near-surface volume mixing ratios and aerosol extinction we find considerably larger relative differences: 10 ± 30, −23 ± 28 and −8 ± 33% for aerosols, HCHO and NO2 respectively. The frequency distributions of these near-surface concentrations show however a quite good agreement, and this indicates that near-surface concentrations derived from MAX-DOAS are certainly useful in a climatological sense. A major difference between the two methods is the dynamic range of retrieved characteristic profile heights which is larger for method B than for method A. This effect is most pronounced for HCHO, where retrieved profile shapes with method A are very close to the a priori, and moderate for NO2 and aerosol extinction which on average show quite good agreement for characteristic profile heights below 1.5 km. One of the main advantages of method A is the stability, even under suboptimal conditions (e.g. in the presence of clouds). Method B is generally more unstable and this explains probably a substantial part of the quite large relative differences between the two methods. However, despite a relatively low precision for individual profile retrievals it appears as if seasonally averaged profile heights retrieved with method B are less biased towards a priori assumptions than those retrieved with method A. This gives confidence in the result obtained with method B, namely that aerosol extinction profiles tend on average to be higher than NO2 profiles in spring and summer, whereas they seem on average to be of the same height in winter, a result which is especially relevant in relation to the validation of satellite retrievals.

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