scholarly journals Robust, Long-term Lake Level Change from Multiple Satellite Altimeters in Tibet: Observing the Rapid Rise of Ngangzi Co over a New Wetland

2019 ◽  
Vol 11 (5) ◽  
pp. 558 ◽  
Author(s):  
Haihong Wang ◽  
Yonghai Chu ◽  
Zhengkai Huang ◽  
Cheinway Hwang ◽  
Nengfang Chao
Keyword(s):  
Time Series ◽  
Lake Level ◽  
Altimeter Data ◽  
Lake Levels ◽  
Outlier Removal ◽  
Water Level Variation ◽  
Waveform Retracking ◽  
Step Algorithm ◽  
Better Than

Satellite altimetry has been successfully applied to monitoring water level variation of global lakes. However, it is still difficult to retrieve accurate and continuous observations for most Tibetan lakes, due to their high altitude and rough terrain. Aiming to generate long-term and accurate lake level time series for the Tibetan lakes using multi-altimeters, we present a robust strategy including atmosphere delay corrections, waveform retracking, outlier removal and inter-satellite bias adjustment. Apparent biases in dry troposphere corrections from different altimeter products are found, and such correctios must be recalculated using the same surface pressure model. A parameter is defined to evaluate the performance of the retracking algorithm. The ICE retracker outperforms the 20% and 50% threshold retrackers in the case of Ngangzi Co, where a new wetland has been established. A two-step algorithm is proposed for outlier removal. Two methods are adopted to estimate inter-satellite bias for different cases of with and without overlap. Finally, a 25-year-long lake level time series of Ngangzi Co are constructed using the TOPEX/Poseidon-family altimeter data from October 1992 to December 2017, resulting in an accuracy of ~17 cm for TOPEX/Poseidon and ~10 cm for Jason-1/2/3. The accuracy of retrieved lake levels is on the order of decimeter. Because of no gauge data available, ICESat and SARAL data with the accuracy better than 7 cm are used for validation. A correlation more than 0.9 can be observed between the mean lake levels from TOPEX/Poseidon-family satellites, ICESat and SARAL. Compared to the previous studies and other available altimeter-derived lake level databases, our result is the most robust and has resulted in the maximum number of continuous samples. The time series indicates that the lake level of Ngangzi Co increased by ~8 m over 1998–2017 and changed with different rates in the past 25 years (-0.39 m/yr in 1992–1997, 1.03 m/yr in 1998–2002 and 0.32 m/yr in 2003–2014). These findings will enhance the understanding of water budget and the effect of climate change.

scholarly journals Detecting Lake Level Change From 1992 to 2019 of Zhari Namco in Tibet Using Altimetry Data of TOPEX/Poseidon and Jason-1/2/3 Missions

2021 ◽  
Vol 9 ◽  
Author(s):  
Mingzhi Sun ◽  
Jinyun Guo ◽  
Jiajia Yuan ◽  
Xin Liu ◽  
Haihong Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Lake Level ◽  
Altimeter Data ◽  
The Tibetan Plateau ◽  
Lake Area ◽  
Lake Levels ◽  
Waveform Retracking ◽  
Gauge Data ◽  
Delay Correction

Zhari Namco, a large lake in the Tibetan Plateau (TP), is sensitive to climate and environmental change. However, it is difficult to retrieve accurate and continuous lake levels for Zhari Namco. A robust strategy, including atmospheric delay correction, waveform retracking, outlier deletion, and inter-satellite adjustment, is proposed to generate a long-term series of lake levels for Zhari Namco through multi-altimeter data. Apparent biases are found in troposphere delay correction from different altimeter products and adjusted using an identical model. The threshold (20%) algorithm is employed for waveform retracking. The two-step method combining a sliding median filter and 2σ criterion is used to eliminate outliers. Tandem mission data of altimeters are used to estimate inter-satellite bias. Finally, a 27-year-long lake level time series of Zhari Namco are constructed using the TOPEX/Poseidon-Jason1/2/3 (T/P-Jason1/2/3) altimeter data from 1992 to 2019, resulting in an accuracy of 10.1 cm for T/P-Jason1/2/3. Temperature, precipitation, lake area, equivalent water height, and in situ gauge data are used for validation. The correlation coefficient more than 0.90 can be observed between this result and in situ gauge data. Compared with previous studies and existing database products, our method yields sequences with the best observational quality and the longest continuous monitoring in Zhari Namco. The time series indicates that the lake level in Zhari Namco has increased by ∼ 5.7 m, with an overall trend of 0.14 ± 0.01 m/yr, showing a fluctuating rate (1992–1999: −0.25 ± 0.05 m/yr, 2000–2008: 0.26 ± 0.04 m/yr, 2009–2016: −0.05 ± 0.03 m/yr, 2017–2019: 1.34 ± 0.34 m/yr). These findings will enhance the understanding of water budget and the effect of climate change in the TP.

Nonstationary Extreme-Value Predictions in the Gulf of Mexico

2007 ◽  
Author(s):  
Christos N. Stefanakos
Keyword(s):  
Time Series ◽  
Gulf Of Mexico ◽  
Wave Height ◽  
Missing Values ◽  
Extreme Value ◽  
Altimeter Data ◽  
Dependence Structure ◽  
Measured Time ◽  
Definition Of

In the present work, return periods of various level values of significant wave height in the Gulf of Mexico are given. The predictions are based on a new method for nonstationary extreme-value calculations that have recently been published. This enhanced method exploits efficiently the nonstationary modeling of wind or wave time series and a new definition of return period using the MEan Number of Upcrossings of the level value x* (MENU method). The whole procedure is applied to long-term measurements of wave height in the Gulf of Mexico. Two kinds of data have been used: long-term time series of buoy measurements, and satellite altimeter data. Measured time series are incomplete and a novel procedure for filling in of missing values is applied before proceeding with the extreme-value calculations. Results are compared with several variants of traditional methods, giving more realistic estimates than the traditional predictions. This is in accordance with the results of other methods that take also into account the dependence structure of the examined time series.

scholarly journals Investigating the long-term evolution of subtropical ozone profiles applying ground-based FTIR spectrometry

2012 ◽  
Vol 5 (11) ◽  
pp. 2917-2931 ◽  
Author(s):  
O. E. García ◽  
M. Schneider ◽  
A. Redondas ◽  
Y. González ◽  
F. Hase ◽  
...  
Keyword(s):  
Time Series ◽  
Line Shape ◽  
Long Term Evolution ◽  
Term Evolution ◽  
Ozone Profile ◽  
Instrumental Line ◽  
Temperature Retrieval ◽  
Linear Trends ◽  
Better Than

Abstract. This study investigates the long-term evolution of subtropical ozone profile time series (1999–2010) obtained from ground-based FTIR (Fourier Transform InfraRed) spectrometry at the Izaña Observatory ozone super-site. Different ozone retrieval strategies are examined, analysing the influence of an additional temperature retrieval and different constraints. The theoretical assessment reveals that the FTIR system is able to resolve four independent ozone layers with a precision of better than 6% in the troposphere and of better than 3% in the lower, middle and upper stratosphere. This total error includes the smoothing error, which dominates the random error budget. Furthermore, we estimate that the measurement noise as well as uncertainties in the applied atmospheric temperature profiles and instrumental line shape are leading error sources. We show that a simultaneous temperature retrieval can significantly reduce the total random errors and that a regular determination of the instrumental line shape is important for producing a consistent long-term dataset. These theoretical precision estimates are empirically confirmed by daily intercomparisons with Electro Chemical Cell (ECC) sonde profiles. In order to empirically document the long-term stability of the FTIR ozone profile data we compare the linear trends and seasonal cycles as obtained from the FTIR and ECC time series. Concerning seasonality, in winter both techniques observe stratospheric ozone profiles that are typical middle latitude profiles (low tropopause, low ozone maximum concentrations) and in summer/autumn profiles that are typical tropical profiles (high tropopause, high maximum concentrations). The linear trends estimated from the FTIR and the ECC datasets agree within their error bars. For the FTIR time series, we observe a significant negative trend in the upper troposphere/lower stratosphere of about −0.2% yr−1 and a significant positive trend in the middle and upper stratosphere of about +0.3% yr−1 and +0.4% yr−1, respectively. Identifying such small trends is a difficult task for any measurement technique. In this context, super-sites applying different techniques are very important for the detection of reliable ozone trends.

Coastal Sea Level Change from in the North Eastern Atlantic

2020 ◽  
Author(s):  
Luciana Fenoglio-Marc ◽  
Bernd Uebbing ◽  
Jürgen Kusche ◽  
Salvatore Dinardo
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Sea Level Change ◽  
Altimeter Data ◽  
Level Change ◽  
The North ◽  
North Eastern ◽  
Coastal Sea

<p>A significant part of the World population lives in the coastal zone, which is affected by coastal sea level rise and extreme events. Our hypothesis is that the most accurate sea level height measurements are derived from the Synthetic Aperture Altimetry (SAR) mode. This study analyses the output of dedicated processing and assesses their impacts on the sea level change of the North-Eastern Atlantic. </p><p>It will be shown that SAR altimetry reduces the minimum usable distance from five to three kilometres when the dedicated coastal retrackers SAMOSA+ and SAMOSA++ are applied to data processed in SAR mode. A similar performance is achieved with altimeter data processed in pseudo low resolution mode (PLRM) when the Spatio-Temporal Altimeter sub-waveform Retracker (STAR) is used. Instead the Adaptive Leading Edge Sub-waveform retracker (TALES) applied to PLRM is less performant. SAR processed altimetry can recover the sea level heights with 4 cm accuracy up to 3-4 km distance to coast. Thanks to the low noise of SAR mode data, the instantaneous SAR and in-situ data have the highest agreement, with the smallest standard deviation of differences and the highest correlation. A co-location of the altimeter data near the tide gauge is the best choice for merging in-situ and altimeter data. The r.m.s. (root mean squared) differences between altimetry and in-situ heights remain large in estuaries and in coastal zone with high tidal regimes, which are still challenging regions. The geophysical parameters derived from CryoSat-2 and Sentinel-3A measurements have similar accuracy, but the different repeat cycle of the two missions locally affects the constructed time-series.</p><p>The impact of these new SAR observations in climate change studies is assessed by evaluating regional and local time series of sea level. At distances to coast smaller than 10 Kilometers the sea level change derived from SAR and LRM data is in good agreement. The long-term sea level variability derived from monthly time-series of LRM altimetry and of land motion-corrected tide gauges agrees within 1 mm/yr for half of in-situ German stations. The long-term sea level variability derived from SAR data show a similar behaviour with increasing length of the time series.</p><p> </p>

scholarly journals NOAA-AVHRR Orbital Drift Correction: Validating Methods Using MSG-SEVIRI Data as a Benchmark Dataset

2021 ◽  
Vol 13 (5) ◽  
pp. 925
Author(s):  
Yves Julien ◽  
José A. Sobrino
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Land Surface ◽  
Correction Method ◽  
Drift Correction ◽  
Noaa Avhrr ◽  
Coarse Spatial Resolution ◽  
Orbital Drift ◽  
Better Than

National Oceanic and Atmospheric Administration–Advanced Very High Resolution Radiometer (NOAA-AVHRR) data provides the possibility to build the longest Land Surface Temperature (LST) dataset to date, starting in 1981 up to the present. However, due to the orbital drift of the NOAA platforms, no LST dataset is available before 2000 and the arrival of newer platforms. Although numerous methods have been developed to correct this orbital drift effect on the LST, a lack of validation has prevented their application. This is the gap we bridge here by using the 15 min temporal resolution of Meteosat Second Generation–Spinning Enhanced Visible and Infra-Red Imager (MSG-SEVIRI) data to simulate drifted and reference LST time series. We then use these time series to validate an orbital drift correction method based on solar zenith angle (SZA) anomalies that we presented in a previous work (C1), as well as two variations of this approach (C0 and C2). Our results show that the C0 method performs better than the two others, although its overall bias absolute value ranges up to 1 K, while standard deviation values remain around 3 K. This is verified for most land covers, for all NOAA platforms, and these statistics remain mostly stable with noise on SZA time series (from 0° to ±10°). With this study, we show that orbital drift correction methods can be thoroughly validated and that such validation should aim toward bias absolute values below 0.1 K and standard deviation values around 1.4 K at coarse spatial resolution. To validate other orbital drift correction approaches, the drifted and reference time series used in this work are freely available for download from the first author’s webpage. This will be the first step toward the building of an orbital-drift-corrected long-term LST dataset.

Predicting Net Discount Rates: A Comparison of Professional Forecasts, Time-series Forecasts and Traditional Methods

2010 ◽  
Vol 21 (2) ◽  
pp. 147-171 ◽  
Author(s):  
Matthew J. Cushing ◽  
David I. Rosenbaum
Keyword(s):  
Time Series ◽  
Random Walk ◽  
Interest Rate ◽  
Discount Rate ◽  
Discount Rates ◽  
Class Of Estimators ◽  
And Performance ◽  
Two Alternatives ◽  
Better Than

Abstract Previous research proposed two future net discount rate estimators that improved on naïve long-term average and random walk estimators. The proposed estimators were superior in the class of estimators that used only current and past observations on net discount rates. In this paper we consider two extensions. First we examine whether professional forecasts perform significantly better than the two alternatives. Second, we examine the properties and performance of multivariate estimators that account for the potentially differing time-series behaviors of the underlying wage growth and interest rate series.

MULTIFRACTAL CROSS-CORRELATION ANALYSIS BASED ON STATISTICAL MOMENTS

Fractals ◽  
2012 ◽  
Vol 20 (03n04) ◽  
pp. 271-279 ◽  
Author(s):  
JING WANG ◽  
PENGJIAN SHANG ◽  
WEIJIE GE
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Cross Correlation ◽  
Moving Average ◽  
Statistical Moments ◽  
Comparative Performance ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
Better Than

We introduce a new method, multifractal cross-correlation analysis based on statistical moments (MFSMXA), to investigate the long-term cross-correlations and cross-multifractality between time series generated from complex system. Efficiency of this method is shown on multifractal series, comparing with the well-known multifractal detrended cross-correlation analysis (MFXDFA) and multifractal detrending moving average cross-correlation analysis (MFXDMA). We further apply this method on volatility time series of DJIA and NASDAQ indices, and find some interesting results. The MFSMXA has comparative performance with MFXDMA and sometimes perform slightly better than MFXDFA. Multifractal nature exists in volatility series. In addition, we find that the cross-multifractality of volatility series is mainly due to their cross-correlations, via comparing the MFSMXA results for original series with those for shuffled series.

scholarly journals Evaluating the Suitability of Century-Long Gridded Meteorological Datasets for Hydrological Modeling

2020 ◽  
Vol 21 (11) ◽  
pp. 2565-2580
Author(s):  
Carolina Massmann
Keyword(s):  
United States ◽  
Time Series ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
The United States ◽  
Model Driven ◽  
Conceptual Hydrological Model ◽  
Better Than

AbstractRecent advances in climate reanalyses have led to the development of meteorological products providing information from the beginning of the last century or even before. As these data sources might be of interest to practitioners in the event of missing data from meteorological stations, it is important to assess their usefulness for different applications. The main objective of this study is to investigate the ability of two long-term reanalysis datasets (CERA-20C and 20CR) and one long-term interpolated dataset (Livneh) for supporting hydrological modeling. The precipitation and temperature data of the three datasets were first compared, downscaled, and then used as inputs to the conceptual hydrological model HBV in 168 basins in the United States. The findings suggest that the quality of all three datasets decreases the further we go back in time. Models calibrated at the beginning of the time series, where the data quality is worse, are only able to capture the general properties of the time series and thus do not show a decrease in performance as the period between calibration and validation becomes larger. The opposite is true for models calibrated at the end of the time series, which show a clear decrease in performance toward the beginning of the century. While the hydrological model driven with the interpolated datasets achieved the best performance, the results obtained with the reanalysis datasets were still informative (i.e., better than the long-term monthly mean), and they matched the performance of the interpolated dataset in a few catchments in the northwestern United States.

scholarly journals Long-term variations in the net inflow record for Lake Malawi

2016 ◽  
Vol 48 (3) ◽  
pp. 851-866 ◽  
Author(s):  
K. Sene ◽  
B. Piper ◽  
D. Wykeham ◽  
R. T. McSweeney ◽  
W. Tych ◽  
...  
Keyword(s):  
Lake Level ◽  
Lake Malawi ◽  
Flood Events ◽  
Lake Levels ◽  
Hydropower Generation ◽  
Unobserved Component ◽  
Periodic Behaviour ◽  
Component Approach ◽  
Long Term Trends

Lake Malawi is the third largest lake in Africa and plays an important role in water supply, hydropower generation, agriculture and fisheries in the region. Lake level observations started in the 1890s and anecdotal evidence of variations dates back to the early 1800s. A chronology of lake level and outflow variations is presented together with updated estimates for the net inflow to the lake. The inflow series and selected rainfall records were also analysed using an unobserved component approach and, although there was little evidence of long-term trends, there was some indication of increasing interannual variability in recent decades. A weak quasi-periodic behaviour was also noted with a period of approximately 4–8 years. The results provide useful insights into the severity of drought and flood events in the region since the 1890s and the potential for seasonal forecasting of lake levels and outflows.

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