Statistical significance of trend estimations for integrated water vapor time series obtained from GPS technique: a case study in Europe

Water vapor is an important medium for the transmission moisture and latent heat in the atmosphere. It is one of the most abundant and dominant greenhouse gases in the atmosphere, which is crucial for global warming. With higher temperatures, the specific humidity will also increase as predicted by the nonlinear Clausius-Clapeyron relationship, indicating a positive feedback loop. Hence, estimation of the trend of Integrated Water Vapor (IWV) in the atmosphere is of great importance for global warming research. However, previous studies have shown that the trends of IWV are usually rather small. Therefore, it is important to estimate the IWV trend and its associated uncertainty with a reasonable mathematical model for the homogenized time series from homogenously reprocessed GPS data sets. Since the 1990s, the Global Positioning System (GPS) has successfully been employed to retrieve IWV with a high temporal resolution, all-weather condition and with global coverage. In this work, we used the hourly GPS Zenith Total Delay (ZTD) time series for 1995.0-2017.0 at 21 European GPS stations derived from a homogeneous data reprocessing. For the conversion of ZTD to IWV, we employed the meteorological variables from ERA5, a state-of-the-art atmosphere reanalysis product newly released by the European Centre for Medium-Range Weather Forecasts (ECMWF). Then, we investigated the influence of noise model assumptions within the mathematical model on the uncertainties of IWV trend estimates. As expected, the results confirmed that the assumption of a white noise only model tends to underestimate the trend uncertainty. A first-order autoregressive process is the preferred mathematical model for a more realistic estimation of the IWV trend uncertainty.

Download Full-text

Estimates of the Water Vapor Climate Feedback during El Niño–Southern Oscillation

Journal of Climate ◽

10.1175/2009jcli3052.1 ◽

2009 ◽

Vol 22 (23) ◽

pp. 6404-6412 ◽

Cited By ~ 26

Author(s):

A. E. Dessler ◽

S. Wong

Keyword(s):

Global Warming ◽

Water Vapor ◽

El Niño ◽

Climate Models ◽

El Nino ◽

Southern Oscillation ◽

Specific Humidity ◽

El Niño Southern Oscillation ◽

El Nino Southern Oscillation ◽

Water Vapor Feedback

Abstract The strength of the water vapor feedback has been estimated by analyzing the changes in tropospheric specific humidity during El Niño–Southern Oscillation (ENSO) cycles. This analysis is done in climate models driven by observed sea surface temperatures [Atmospheric Model Intercomparison Project (AMIP) runs], preindustrial runs of fully coupled climate models, and in two reanalysis products, the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) and the NASA Modern Era Retrospective-Analysis for Research and Applications (MERRA). The water vapor feedback during ENSO-driven climate variations in the AMIP models ranges from 1.9 to 3.7 W m−2 K−1, in the control runs it ranges from 1.4 to 3.9 W m−2 K−1, and in the ERA-40 and MERRA it is 3.7 and 4.7 W m−2 K−1, respectively. Taken as a group, these values are higher than previous estimates of the water vapor feedback in response to century-long global warming. Also examined is the reason for the large spread in the ENSO-driven water vapor feedback among the models and between the models and the reanalyses. The models and the reanalyses show a consistent relationship between the variations in the tropical surface temperature over an ENSO cycle and the radiative response to the associated changes in specific humidity. However, the feedback is defined as the ratio of the radiative response to the change in the global average temperature. Differences in extratropical temperatures will, therefore, lead to different inferred feedbacks, and this is the root cause of spread in feedbacks observed here. This is also the likely reason that the feedback inferred from ENSO is larger than for long-term global warming.

Download Full-text

Diurnal Cycle of Convective Instability around the Central Mountains in Japan during the Warm Season

Journal of the Atmospheric Sciences ◽

10.1175/jas3423.1 ◽

2005 ◽

Vol 62 (5) ◽

pp. 1626-1636 ◽

Cited By ~ 42

Author(s):

Tomonori Sato ◽

Fujio Kimura

Keyword(s):

Water Vapor ◽

Diurnal Cycle ◽

Convective Instability ◽

Potential Temperature ◽

Warm Season ◽

Precipitable Water ◽

Specific Humidity ◽

High Temporal Resolution ◽

Mountainous Areas ◽

Diurnal Cycles

Abstract Convective rainfall often shows a clear diurnal cycle. The nighttime peak of convective activity prevails in various regions near the world's mountains. The influence of the water vapor and convective instability upon nocturnal precipitation is investigated using a numerical model and observed data. Recent developments in GPS meteorology allow the estimation of precipitable water vapor (PWV) with a high temporal resolution. A dense network has been established in Japan. The GPS analysis in August 2000 provides the following results: In the early evening, a high-GPS-PWV region forms over mountainous areas because of the convergence of low-level moisture, which gradually propagates toward the adjacent plain before midnight. A region of convection propagates simultaneously eastward into the plain. The precipitating frequency correlates fairly well with the GPS-PWV and attains a maximum value at night over the plain. The model also provides similar characteristics in the diurnal cycles of rainfall and high PWV. Abundant moisture accumulates over the mountainous areas in the afternoon and then advects continuously toward the plain by the ambient wind. The specific humidity greatly increases at about the 800-hPa level over the plain at night, and the PWV reaches its nocturnal maximum. The increase in the specific humidity causes an increase of equivalent potential temperature at about the 800-hPa level; as a result, the convective instability index becomes more unstable over the plain at night. These findings are consistent with the diurnal cycle of the observed precipitating frequency.

Download Full-text

Noise characteristics in Zenith Total Delay from homogeneously reprocessed GPS time series

10.5194/amt-2016-385 ◽

2016 ◽

Cited By ~ 4

Author(s):

Anna Klos ◽

Addisu Hunegnaw ◽

Felix Norman Teferle ◽

Kibrom Ebuy Abraha ◽

Furqan Ahmed ◽

...

Keyword(s):

Time Series ◽

White Noise ◽

Autoregressive Process ◽

Noise Model ◽

Total Delay ◽

Tropical Zone ◽

Climate Zones ◽

Zenith Total Delay ◽

Noise Characteristics ◽

Gps Time Series

Abstract. Zenith Total Delay (ZTD) time series, derived from the re-processing of Global Positioning System (GPS) data, provide valuable information for the evaluation of global atmospheric reanalysis products such as ERA-Interim. Identifying the correct noise characteristics in the ZTD time series is an important step to assess the "true" magnitude of ZTD trend uncertainties. The ZTD residual time series for 1995–2015 are generated from our homogeneously re-processed and homogenized GPS time series from over 700 globally distributed stations classified into five major climate zones. The annual peak of ZTD data ranges between 10 and 150 mm with the smallest values for the polar and Alpine zone. The amplitudes of daily curve fall between 0 and 12 mm with the greatest variations for the dry zone. The autoregressive process of fourth order plus white noise model were found to be optimal for ZTD series. The tropical zone has the largest amplitude of autoregressive noise (9.59 mm) and the greatest amplitudes of white noise (13.00 mm). All climate zones have similar median coefficients of AR(1) (0.80 ± 0.05) with a minimum for polar and Alpine, which has the highest coefficients of AR(2) (0.27 ± 0.01) and AR(3) (0.11 ± 0.01) and clearly different from the other zones considered. We show that 53 of 120 examined trends became insignificant, when the optimum noise model was employed, compared to 11 insignificant trends for pure white noise. The uncertainty of the ZTD trends may be underestimated by a factor of 3 to 12 compared to the white noise only assumption.

Download Full-text

Assessment of Automatically Monitored Water Levels and Water Quality Indicators in Rivers with Different Hydromorphological Conditions and Pollution Levels in Greece

Hydrology ◽

10.3390/hydrology8020086 ◽

2021 ◽

Vol 8 (2) ◽

pp. 86

Author(s):

Angeliki Mentzafou ◽

George Varlas ◽

Anastasios Papadopoulos ◽

Georgios Poulis ◽

Elias Dimitriou

Keyword(s):

Water Quality ◽

Time Series ◽

Quality Indicators ◽

High Frequency ◽

Automatic Monitoring ◽

Water Levels ◽

High Temporal Resolution ◽

Water Quality Indicators ◽

Pollution Levels ◽

Riverine Ecosystems

Water resources, especially riverine ecosystems, are globally under qualitative and quantitative degradation due to human-imposed pressures. High-temporal-resolution data obtained from automatic stations can provide insights into the processes that link catchment hydrology and streamwater chemistry. The scope of this paper was to investigate the statistical behavior of high-frequency measurements at sites with known hydromorphological and pollution pressures. For this purpose, hourly time series of water levels and key water quality indicators (temperature, electric conductivity, and dissolved oxygen concentrations) collected from four automatic monitoring stations under different hydromorphological conditions and pollution pressures were statistically elaborated. Based on the results, the hydromorphological conditions and pollution pressures of each station were confirmed to be reflected in the results of the statistical analysis performed. It was proven that the comparative use of the statistics and patterns of the water level and quality high-frequency time series could be used in the interpretation of the current site status as well as allowing the detection of possible changes. This approach can be used as a tool for the definition of thresholds, and will contribute to the design of management and restoration measures for the most impacted areas.

Download Full-text

Analysis of Noise and Velocity in GNSS EPN-Repro 2 Time Series

Remote Sensing ◽

10.3390/rs13142783 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2783

Author(s):

Sorin Nistor ◽

Norbert-Szabolcs Suba ◽

Kamil Maciuk ◽

Jacek Kudrys ◽

Eduard Ilie Nastase ◽

...

Keyword(s):

Time Series ◽

Visual Inspection ◽

Flicker Noise ◽

Vertical Component ◽

Likelihood Estimation ◽

Noise Model ◽

Noise Amplitude ◽

Allan Deviation ◽

Power Spectral ◽

Position Time Series

This study evaluates the EUREF Permanent Network (EPN) station position time series of approximately 200 GNSS stations subject to the Repro 2 reprocessing campaign in order to characterize the dominant types of noise and amplitude and their impact on estimated velocity values and associated uncertainties. The visual inspection on how different noise model represents the analysed data was done using the power spectral density of the residuals and the estimated noise model and it is coherent with the calculated Allan deviation (ADEV)-white and flicker noise. The velocities resulted from the dominant noise model are compared to the velocity obtained by using the Median Interannual Difference Adjusted for Skewness (MIDAS). The results show that only 3 stations present a dominant random walk noise model compared to flicker and powerlaw noise model for the horizontal and vertical components. We concluded that the velocities for the horizontal and vertical component show similar values in the case of MIDAS and maximum likelihood estimation (MLE), but we also found that the associated uncertainties from MIDAS are higher compared to the uncertainties from MLE. Additionally, we concluded that there is a spatial correlation in noise amplitude, and also regarding the differences in velocity uncertainties for the Up component.

Download Full-text

A Critical Review on Mathematical Descriptions to Study Flux Processes and Environmental-Related Interactions of Mangroves

Sustainability ◽

10.3390/su13126970 ◽

2021 ◽

Vol 13 (12) ◽

pp. 6970

Author(s):

Jefferson Brooks ◽

Miguel Chen Chen Austin ◽

Dafni Mora ◽

Nathalia Tejedor-Flores

Keyword(s):

Systematic Review ◽

Mathematical Model ◽

Global Warming ◽

Process Modeling ◽

Critical Review ◽

Systematic Search ◽

Raw Material ◽

Mangrove Forests ◽

Recent Information ◽

Modeling Studies

Trees are resources that provide multiple benefits, such as the conservation of fauna, both terrestrial and marine, a source of food and raw material, and offering protection in storms, which makes it practical to understand their behavior against different phenomena. Such understanding may be possible through process modeling. Studies confirm that mangrove forests can store more carbon than other forests, influencing the fight against global warming. Thus, a critical and systematic review was carried out regarding studies focusing on mangroves to collect information on the models that have been applied and the most influential variables highlighted by other authors. Applying a systematic search for the most relevant topics related to mangroves (basic as well as recent information), it is possible to group models and methods carried out by other authors to respond to certain behaviors presented by mangroves. Moreover, possible structuring of a mathematical model applied to a species of interest thanks to the analyzed references could provide justified information to the authorities on the importance of these forests and the benefits of their preservation and regeneration-recovery.

Download Full-text

Business Days Time Series Weekly Trend and Seasonality

Engineering Proceedings ◽

10.3390/engproc2021005026 ◽

2021 ◽

Vol 5 (1) ◽

pp. 26

Author(s):

Karlis Gutans

Keyword(s):

Time Series ◽

Global Warming ◽

Exchange Rates ◽

Temperature Data ◽

Adjustment Method ◽

Currency Exchange ◽

Currency Exchange Rates ◽

Time Series Decomposition ◽

The World

The world changes at incredible speed. Global warming and enormous money printing are two examples, which do not affect every one of us equally. “Where and when to spend the vacation?”; “In what currency to store the money?” are just a few questions that might get asked more frequently. Knowledge gained from freely available temperature data and currency exchange rates can provide better advice. Classical time series decomposition discovers trend and seasonality patterns in data. I propose to visualize trend and seasonality data in one chart. Furthermore, I developed a calendar adjustment method to obtain weekly trend and seasonality data and display them in the chart.

Download Full-text

Assessment of Sampling Effects on Various Satellite-Derived Integrated Water Vapor Datasets Using GPS Measurements in Germany as Reference

Remote Sensing ◽

10.3390/rs12071170 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1170 ◽

Cited By ~ 3

Author(s):

Cintia Carbajal Henken ◽

Lisa Dirks ◽

Sandra Steinke ◽

Hannes Diedrich ◽

Thomas August ◽

...

Keyword(s):

Water Vapor ◽

Pairwise Comparison ◽

Weather Conditions ◽

Satellite System ◽

High Temporal Resolution ◽

Data Sets ◽

Frequency Distributions ◽

Temporal Sampling ◽

Global Navigation Satellite ◽

Satellite Instruments

Passive imagers on polar-orbiting satellites provide long-term, accurate integrated water vapor (IWV) data sets. However, these climatologies are affected by sampling biases. In Germany, a dense Global Navigation Satellite System network provides accurate IWV measurements not limited by weather conditions and with high temporal resolution. Therefore, they serve as a reference to assess the quality and sampling issues of IWV products from multiple satellite instruments that show different orbital and instrument characteristics. A direct pairwise comparison between one year of IWV data from GPS and satellite instruments reveals overall biases (in kg/m 2 ) of 1.77, 1.36, 1.11, and −0.31 for IASI, MIRS, MODIS, and MODIS-FUB, respectively. Computed monthly means show similar behaviors. No significant impact of averaging time and the low temporal sampling on aggregated satellite IWV data is found, mostly related to the noisy weather conditions in the German domain. In combination with SEVIRI cloud coverage, a change of shape of IWV frequency distributions towards a bi-modal distribution and loss of high IWV values are observed when limiting cases to daytime and clear sky. Overall, sampling affects mean IWV values only marginally, which are rather dominated by the overall retrieval bias, but can lead to significant changes in IWV frequency distributions.

Download Full-text

LamaH: Large-sample Data for Hydrology in Central Europe

10.5194/egusphere-egu21-14335 ◽

2021 ◽

Author(s):

Christoph Klingler ◽

Mathew Herrnegger ◽

Frederik Kratzert ◽

Karsten Schulz

Keyword(s):

Time Series ◽

Time Series Data ◽

Vegetation Indices ◽

River Network ◽

Series Data ◽

High Temporal Resolution ◽

Stream Length ◽

Discharge Data ◽

Large Sample ◽

Access To Data

Open large-sample datasets are important for various reasons: i) they enable large-sample analyses, ii) they democratize access to data, iii) they enable large-sample comparative studies and foster reproducibility, and iv) they are a key driver for recent developments of machine-learning based modelling approaches.Recently, various large-sample datasets have been released (e.g. different country-specific CAMELS datasets), however, all of them contain only data of individual catchments distributed across entire countries and not connected river networks.Here, we present LamaH, a new dataset covering all of Austria and the foreign upstream areas of the Danube, spanning a total of 170.000 km&#178; in 9 different countries with discharge observations for 882 gauges. The dataset also includes 15 different meteorological time series, derived from ERA5-Land, for two different basin delineations: First, corresponding to the entire upstream area of a particular gauge, and second, corresponding only to the area between a particular gauge and its upstream gauges. The time series data for both, meteorological and discharge data, is included in hourly and daily resolution and covers a period of over 35 years (with some exceptions in discharge data for a couple of gauges).Sticking closely to the CAMELS datasets, LamaH also contains more than 60 catchment attributes, derived for both types of basin delineations. The attributes include climatic, hydrological and vegetation indices, land cover information, as well as soil, geological and topographical properties. Additionally, the runoff gauges are classified by over 20 different attributes, including information about human impact and indicators for data quality and completeness. Lastly, LamaH also contains attributes for the river network itself, like gauge topology, stream length and the slope between two sequential gauges.Given the scope of LamaH, we hope that this dataset will serve as a solid database for further investigations in various tasks of hydrology. The extent of data combined with the interconnected river network and the high temporal resolution of the time series might reveal deeper insights into water transfer and storage with appropriate methods of modelling.

Download Full-text